Things span , for instance , RFID tags , sensors , computers , plants , lamps , autonomous robots , and self-driving vehicles . Often , things are connected through heterogeneous platforms also providing support for , e.g. , data collection , management , and applications deployment . Additionally , things can offer their functionalities as ( web ) services facilitating their dynamic interaction . Heterogeneous platforms are now becoming increasing omnipresent due to the availability of multicores at commodity prices . In order to benefit from the immense parallel capability of these multicores , more and more applications are now being developed as well as ported using the CUDA framework that programs these cores . In this paper we analyze the limitations of collective MPI communication for the computation of fast Fourier transforms ( FFTs ) , which are relied on heavily for large-scale particle simulations . We present experiments made at one of the largest heterogeneous platforms , the Summit supercomputer at ORNL . We discuss communication models from state-of-the-art FFT libraries , and propose a new FFT library , named HEFFTE ( Highly Efficient FFTs for Exascale ) , which supports heterogeneous architectures and yields considerable speedups compared with CPU libraries , while maintaining good weak as well as strong scalability . The Heterogeneous System Architecture is established by the HSA Foundation to address many of the current shortcomings at a system architecture and programming model level while providing a great foundation for already established SW models , and in addition to the GPU allow extending the architecture to other specialty processors like DSPs , FPGAs and others to interoperate within the SW framework , a main task for the next level of work in the HSA Foundation . The HSA Foundation , a not-for-profit consortium of SOC and SOC IP vendors , OEMs , academia , OSVs and ISVs defining a consistent heterogeneous platform architecture to make it dramatically easier to program heterogeneous parallel devices like GPUs and other accelerators . The presentation gives the audience a high-level understanding of the goals of HSA , the HSA system architecture properties and its use models by system software , tools and applications . We present a framework ( real-time calculus ) for analysing various system properties pertaining to timing analysis , loads on various components and on-chip buffer memory requirements of heterogeneous platform-based architectures , in a single coherent way . Many previous analysis techniques from the real-time systems domain , which are based on standard event models , turn out to be special cases of our framework . Especially if programmable CPUs are combined with reconfigurable logic like it has been done in the embedded domain , the complexity to design an energy efficient and powerful system increases . Therefore , heterogeneous system platforms have to be focused even stronger in research and education . Because the design and programming of these architectures is much more complex than using standard processor solutions , it is essential to provide thorough education programs for students . Furthermore , the architecture also provides a mixed flavor of homogeneous general-purpose processing and heterogeneous reconfigurable computing . Prior approaches have mostly considered homogeneous and heterogeneous platforms as two different design paradigms despite both show domain-specific performance advantages over each other . In this context , the proposed architecture is designed for nine NoC nodes , arranged in a topology of three rows and three columns . As part of the Oak Ridge Leadership Computing Facility (OLCF)-3 project , the Oak Ridge National Laboratory ( ORNL ) is working with several vendors and engaged in research to develop a Programming Environment ( PE ) for mixed CPU/GPU based ultra-scale computer systems . The environment provides a toolset to port or develop CPU/GPU systems while reducing development time to improve the performance and portability of the codes while minimizing sources of errors . Our toolset consists of compilers for high-level GPU directives , libraries , performance tools , and a debugger with synergistic interfaces among them . Fourth , at each iteration of the hybrid parallel strategy , the solution vectors of individuals are transferred to the GPU and their costs are transferred back to the CPU . In order to further improve the efficiency of proposed algorithm , we propose an asynchronous transfer pattern ( stream concurrency pattern ) for CPU-GPU , in which the transfer process and computation process are overlapped and eventually the overall run-time can be reduced further . Finally , the experiments show that the solution quality obtained by our method is competitive with existing heuristic methods in reasonable time . In the PC technology , CPU and GPU is always complement each other , in the two have developed to the emergence of new bottlenecks , the"integration"is probably the sensible solution , but on the integration of the program on the CPU and the GPU has been talked about by people . This paper studies the CPU + GPU , heterogeneous computing algorithms advantages and the possibility of future applications , especially with the general-purpose computer program interface(OpenCL)release , CPU + GPU , heterogeneous computing that looks like a CPU and the emergence of a mixture of GPU I believe this will allow a computer processor in turn to a new level , the achievements of this isomerization is more high-performance , A more cost-effective processor , which will surely mark the climax of the revolutionary GPU and CPU . Specifically , we analyse the bottlenecks in automated and autonomous vehicles and find the on-car computer is the key component for low latency and low power self-driving vehicles . We show detailed research developments on the most advanced CPU-GPU platform including four aspects , mainly putting focus on the methods to reduce latency and energy in the system . The four aspects cover reducing data transferring overhead , reducing power consumption with power-gating , optimizing warp scheduling scheme and optimizing cache performance in coprocessors . Therefore , throughout this research , we proposed a CPU-GPU architecture for ANC algorithms , which does not suffer from the problems of frequency domain algorithms and take advantage of GPU computation . Also , we suggested important factors needed to be concerned when an ANC algorithm based on CPU-GPU architecture is being developed . The feasibility of the proposed CPU-GPU architecture for ANC algorithms is shown with an example of implementing the proposed CPU-GPU architecture on multichannel delayless subband ANC algorithm . In the previous research , as a way of overcoming the lack of computational power , CPU-GPU architecture was proposed so that ANC algorithms utilize the massive computing power of GPU without suffering from the block data transfer between CPU and GPU memories . However , for the feasibility test of the proposed CPU-GPU architecture in the previous research , a conventional block ANC algorithm was used , and ANC algorithm which can fully utilize the massive computing power of GPU has not been developed . In this article , ANC algorithm , which directly derives blockwise least square solution through GPU computation while generating control signals through CPU computation , is proposed . Using detailed thermal and power maps , we demonstrate that in contrast to traditional multi-core CPUs heterogeneous processors exhibit higher intertwined behavior for dynamic voltage and frequency scaling ( DVFS ) and workload scheduling , in terms of their effect on performance , power and temperature . Second , we propose a framework to map workloads on appropriate device of CPU-GPU processors under different static and time-varying workload/system conditions . We implement the scheduler on a real CPU-GPU processor , and using OpenCL benchmarks , we demonstrate up to 24 % runtime improvement and 10 % energy savings compared to the state-of-the-art scheduling techniques . Modern CPU-GPU processors enable workloads to run on both CPU and GPU devices . Current scheduling practices mainly use the characteristics of kernel workloads to decide the CPU/GPU mapping . The experimental result shows that our approach achieved an overall 48.84-fold speedup ratio in ZY-3 nadir image preprocessing ( the corresponding run time is 11.60 s for one image ) , which is capable of meeting the requirement of near real-time response to the applications that follow . In addition , with the supportability of IEEE 754–2008 floating-point standard in the Fermi type GPU , preprocessing ZY-3 images with our CPU/GPU processors could maintain the quality of image preprocess as done traditionally with CPU processors . NVIDIA Compute Unified Device Architecture (CUDA)-assisted central processing unit ( CPU ) and graphics processing unit ( GPU ) computing platform has proven its potential to be used for HPC supports . In CPU/GPU computing , original data and instructions are copied from CPU-main-memory to GPU-global-memory . Inside GPU , it would be beneficial to keep the data into shared memory ( shared only by the threads of that block ) than in the global memory ( shared by all threads ) . Our approach , on the other hand , uses active learning to carefully select the more informative examples , thus reducing the training time . We demonstrate this technique by automatically creating a model to determine on which device to execute four parallel programs at differing problem dimensions for a representative Cpu – Gpu based system . Our methodology is remarkably simple and yet effective , making it a strong candidate for wide adoption . Quasar consists of a high-level language , a corresponding integrated development environment ( IDE ) , a compiler , and a run-time system . Its aim is to relieve the programmer from hardware-related implementation issues that commonly occur in CPU/GPU programming . Examples include selecting memory types , memory management , concurrent kernel/function execution , hardware texturing units , etc . In this demonstration , we present a new programming framework , Quasar , for heterogeneous programming on CPU and single/multi-GPU . Our programming framework consists of a high-level language that is aimed at relieving the programmer from hardware-related implementation issues that commonly occur in CPU/GPU programming , allowing the programmer to focus on the specification , the design , testing and the improvement of the algorithms . We will demonstrate a real-time multi-camera processing application using our integrated development environment ( IDE ) . We evaluated this control on an Intel Baytrail-based Android platform . Experimental evaluations show energy savings of 17.4 % in the CPU-GPU subsystem with a low performance impact of 0.9 % . Furthermore , Exascale computing is characterised by a " democratisation " of HPC : to fully exploit the capabilities of Exascale-level facilities , HPC is moving towards enabling access to its resources to a wider range of new players , including SMEs , through cloud-based approaches [ 1 ] . Finally , the need for much higher energy efficiency is pushing towards deep heterogeneity , widening the range of options for acceleration , moving from the traditional CPU-only organization , to the CPU plus GPU which currently dominates the Green5001 , to more complex options including programmable accelerators and even ( reconfigurable ) hardware accelerators [ 2 ] . For example , in our real system we need to predict hundreds of ads for each visitor in less than 10 milliseconds . In our practice , several important techniques are deployed for accelerating online serving of industrial deep networks under the CPU - GPU architecture : i ) request batching which merges adjacent requests from CPU to take advantage of GPU power , ii ) GPU memory optimization which improves the access pattern to reduce wasted transactions in GPU memory , iii ) concurrent kernel computation which allows execution of matrix computations to be processed with multiple CUDA kernels concurrently . Heterogeneous computing platforms consisting of general purpose processors ( GPPs ) and graphics processing units ( GPUs ) have become commonplace in personal mobile devices and embedded systems . For years , programming of these platforms was very tedious and simultaneous use of all available GPP and GPU resources required low-level programming to ensure efficient synchronization and data transfer between processors . Abstract Despite maturing in many ways , heterogeneous distributed computing platforms continue to require substantial effort in terms of software installation and management for efficient use , often necessitating manual intervention by resource providers and end-users . In this paper we propose a novel model of resource sharing that is a viable alternative to that commonly adopted in the grid community . A Performance Opportunity . The class of modern datacenters recently coined as “ warehouse scale computers ” ( WSCs ) has traditionally been embraced as homogeneous computing platforms . However , due to frequent machine replacements and upgrades , modern WSCs are in fact composed of diverse commodity microarchitectures and machine configurations . The importance of high level management of complex information systems , extending over several heterogeneous and geographically distributed computing platforms , is rapidly increasing . The design of a comprehensive and enterprise-wide security strategy , including the efficient administration of large user groups while maintaining the access rights of tens of thousands of users , is next to impossible without support from sophisticated and powerful software toolsets . Sensor networks are being applied in several emerging sophisticated applications due to the use of powerful and high-quality sensor nodes , such as radars and visible light cameras . However , these nodes need additional features to optimally benefit from heterogeneous modern computing platforms . Therefore , reconfigurable computing is a potential paradigm for those scenarios as it can provide flexibility to explore the computational resources on that kind of high performance computing system . Abstract The PVM system , a software framework for heterogeneous concurrent computing in networked environments , has evolved in the past several years into a viable technology for distributed and parallel processing in a variety of disciplines . PVM supports a straightforward but functionally complete message passing model , and is capable of harnessing the combined resources of typically heterogeneous networked computing platforms to deliver high levels of performance and functionality . In this paper , we describe the architecture of PVM system , and discuss its computing model , the programming interface it supports , auxiliary facilities for process groups and MPP support , and some of the internal implementation techniques employed . Being a well-accepted protocol in the industry , the proposed solution integrates existing modules that use the RS232 interface without having to redesign the whole communication system . Implementation platforms used for testing solution include Xilinx reconfigurable platforms , namely Spartan3 and Virtex FPGAs , as well as low cost microcontrollers , namely Microchip PIC18F4620 , and general purpose PCs ; industrial PCs , PLCs or other platforms with an RS-232 serial port can be easily integrated . A ring topology was selected to allow greater flexibility . We have implemented our architecture in FPGAs and ASICs . In FPGAs platform , the new architecture increases the throughput by 45 % , compared with the latest work , and particularly the new architecture also saves nearly 12 % of hardware resources . In the 65 nm ASIC technology , the throughput of the new design can up to 80Gbps , which is 2.7 times faster than the fastest one in the literature , in particular , it also saves at least 40 % of hardware resources . Two case studies , involving modern FPGA and floating-point applications , are used to illustrate our approach . Compared with VPR5.0 and VPR6.0 , the architecture file format adopted by Hydrate is often much smaller and easier to understand , and over 90 % of file size reduction for complex FPGAs can be achieved . Moreover , Hydrate is compatible with different versions of VPR , so that the powerful VPR tool flow can be used to explore future architectures . However , suitable hardware has begun to appear . Complex FPGAs now provide an adequate level of speed , density and programmability in the form of reconfigurable computers , boards , and chips with embedded computational support . Such hardware could allow rapid implementation and change of systolic algorithms leading to inexpensive " programmable " systolic array hardware . Results of the AGC algorithms on the IR images obtained by the 3 - 5 micron hand held thermal imager are discussed . The simulation results calls for hardware implementation using Xilinx SPARTAN-II FPGAs . The uniqueness of this paper lies in two features : one is the improvement suggested on the adaptive plateau equalization algorithm and the other is the hardware implementation approach . In this paper , we propose a target board architecture suitable for embedded signal processing applications based on hardware software codesign . The target board , which serves as a system attached to a host PC via a PCI bus interface , contains a TMS320C30 DSP processor and up to four Xilinx XC5204 FPGAs . The software and hardware sections of the codesign can be easily implemented using C and VHDL programming in the C30 processor and FPGAs , respectively . Internally , a pipeline of hardware modules for a domain-specific application is implemented in the FPGAs , where they are directly connected through their serial transceiver links . The necessary inter-FPGA communication subsystem with a flow control mechanism is implemented with Intel Arria 10 FPGAs , where the resource consumption and sustained network throughput are obtained , which averages at 7.92 GB/s . Performance estimation of a fluid simulation using the measured inter-FPGA network parameters is shown for a varied pipeline depth with explored temporal and spatial parallel options . In this paper we propose a hardware architecture of the Fuzzy ART 's generalization applied to the classification of multi-spectral image acquired by the Algerian satellite ALSAT 1 . The developed solution is original and exploits the maximum benefit of the embedded resources in the reconfigurable circuits FPGAs . The goal is the research of a scalable architecture which gives a very good quality of classification and a very weak computing time ( about a microsecond ) with a very limited occupation of the logical resources . A prototype of the compressor is available in software , but it runs at a speed that does not meet spacecraft requirements . The hardware implementation targets the Xilinx Virtex IV FPGAs , and makes the use of this compressor practical for Earth satellites as well as beyond-Earth missions with hyperspectral instruments . Our research investigates how a commercial FPGA ’s fixed interconnect and CAD flow constrain the performance of NoCs based on a set of design parameters . We develop an analytical model that predicts the performance for both homogeneous and heterogeneous NoCs with a geometric mean error of 4.68 % for Xilinx Virtex 2 Pro , Virtex 4 , Virtex 5 , and Virtex 6 FPGAs , and with a geometric mean error of 5.12 % for Altera Stratix III and Stratix IV FPGAs . However , the recent advances in InfiniBand fabrics have changed this trend . In this paper we analyze how the high bandwidth provided by the new EDR 100 G InfiniBand fabric allows remote GPU virtualization middleware systems not only to perform very similar to local GPUs , but also to improve overall performance for some applications . For instance , the use of remote GPUs allows non-MPI applications to use a number of GPUs larger than those that can fit into a single server box . In this way , non-distributed applications may leverage all the GPUs present in the cluster , thus boosting their performance beyond the limits that the use of local GPUs may report [ 1 ] . On the other hand , the remote GPU virtualization mechanism easily allows a cluster that does not include GPUs to be extended with GPUs just by attaching one or several GPU-based servers to the cluster . Following the MPEG CDVS standard , this paper presents an efficient feature computation phase , completely implemented on embedded devices supporting the OpenCL framework . Following our contribution to the MPEG-CDVS standard , we present the new born CDVS detector and its design for multi- core parallel GPUs . We show how to adjust algorithmic choices and implementation details to target the intrinsic characteristics of the embedded platforms selected . Nvidia 's Tesla C2050 , codenamed Fermi , and AMD 's Radeon 5870 are two devices positioned to meet the computationally demanding needs of supercomputing research groups across the globe . Though Nvidia GPUs powered by CUDA have been the frequent choices of the performance centric research groups , the introduction and growth of OpenCL has promoted AMD GP-GPUs as potential accelerator candidates that can challenge Nvidia 's stronghold . These architectures not only offer a plethora of features for application developers to explore , but their radically different architectures calls for a detailed study that weighs their merits and evaluates their potential to accelerate complex scientific applications . Graphics Processing Units ( GPU ) have recently been the subject of attention in research as an efficient coprocessor for implementing many classes of highly parallel applications . The GPUs design is engineered for graphics applications , where many independent SIMD workloads are simultaneously dispatched to processing elements . While parallelism has been explored in the context of traditional CPU threads and SIMD processing elements , the principles involved in dividing the steps of a parallel algorithm for execution on GPU architectures remains a significant challenge . Wide SIMD-based GPUs have evolved into a promising platform for running general purpose workloads . Current programmable GPUs allow even code with irregular control to execute well on their SIMD pipelines . This network effectively solved the imbalance problem between foreground and background voxels by proposing a loss function based on the Dice similarity metric . In this work , we extend the depth of the original V-Net to obtain better features to model the increased complexity of multi-class segmentation tasks at higher input/output resolutions using modern large-memory GPUs . Furthermore , we markedly improved the training behaviour of V-Net by employing batch normalization layers throughout the network . Biomedical application speed requirements have made general purpose graphics processing unit ( GPU ) acceleration of Biomedical and Bioinformatics a lucrative area of study . SIMD design of GPUs means that most Biomedical applications are either not suitable for acceleration on GPUs , or can not reach their full acceleration potential due to thread divergence . Also , many Biomedical applications are decision heavy and experience intense thread divergence on GPUs . Learn the history and capabilities of GPU‐based computing systems 2 . Learn the hardware organization of GPUs 3 . Learn tools and procedures for programming GPUs We have been witnessing a continuous growth of both heterogeneous computational platforms ( e.g. , Cell blades , or the joint use of traditional CPUs and GPUs ) and multi- core processor architecture ; and it is still an open question how applications can fully exploit such computational potential efficiently . In this paper we introduce a run-time environment and programming framework which supports the implementation of scalable and efficient parallel applications in such heterogeneous , distributed environments . Unfortunately , access to domain experts at the same physical location is not always possible and new tools and techniques are required to facilitate simultaneous and collaborative exploration of data between spatially separated experts . This paper presents a framework for collaborative visualization of biomedical data-sets , supporting heterogeneous computational platforms and network configurations . The system provides the user with data visualization , annotation and the middleware to exchange the resulting visuals between all participants , in real-time . In this paper we present a workflow solution to support graphically the design , execution , monitoring , and performance visualisation of complex grid applications . The described workflow concept can provide interoperability among different types of legacy applications on heterogeneous computational platforms , such as Condor or Globus based grids . The major design and implementation issues concerning the integration of Condor tools , Mercury grid monitoring infrastructure , PROVE performance visualisation tool , and the new workflow layer of P-GRADE are discussed in two scenarios . Our initial experiments indicate that an 8 node heterogeneous cluster can realize over 100times speedup compared to a high performance uniprocessor system . By creating a platform based on modern hardware , we believe that a heterogeneous compute platform customized for image registration can provide robust , scalable , cost effective sub-minute medical image registration capabilities . Traditional hardware evelopers can still port their existing RTL to ACAP via the traditional RTL entry flow . This talk reviews the needs driving the change from the traditional CPU-based compute model , explores the alternative options , and unveils the Xilinx Versal ACAP , the industry 's first heterogeneous compute platform . Raising the Abstraction Level . We present an overview of the evolution of programming techniques for Field-Programmable Gate Arrays ( FPGAs ) , with a particular focus on High-Level Synthesis ( HLS ) and Heterogeneous Computing ( HC ) , and we argue that , in the context of High-Performance Computing ( HPC ) , FPGAs should be treated as components of a larger heterogeneous compute platform . Consequently , HLS and HC tools become compilation targets rather than high-level development tools . Smart cyber-physical systems ( CPSs ) have complex interaction with their environment which is rarely known in advance , and they heavily depend on intelligent data processing carried out over a heterogeneous and distributed computation platform with resource-constrained devices to monitor , manage and control autonomous behavior . First , we propose a distributed runtime model to capture the operational state and the context information of a smart CPS using directed , typed and attributed graphs as high-level knowledge representation . Bioinformatics workloads are characterized by huge data sets and complex algorithms , requiring enormous data processing and making high performance heterogeneous computation platforms such as FPGAs and GPUs highly relevant . We compare three accelerated implementations of the widely used BWA-MEM genomic mapping tool as a case study on design-time optimization for heterogeneous architectures : BWA-MEM-CUDA , BWA-MEM-OpenCL , and BWA-MEMVHDL , each using an optimized Smith-Waterman algorithm implementation . For this reason we propose a flow , which assists the software developer at design time , offering immediate power- and performance-estimation . Such approaches are already known in the embedded world , helping there to select between different design possibilities , and will be used to get the best possible performance from a massively heterogeneous computation platform , while still keeping the energy consumption in mind . To address this question , an open-source neuromorphic processor architecture simulator called NeMo is being developed . This effort will enable the design space exploration of potential heterogeneous compute systems that combine traditional CPUs , GPUs , and neuromorphic hardware . This article examines the design , implementation , and performance of NeMo . Significant application performance improvements can be achieved by heterogeneous compute technologies , such as multi-core CPUs , GPUs and FPGAs . The HARNESS project is developing architectural principles that enable the next generation cloud platforms to incorporate such devices thereby vastly increasing performance , reducing energy consumption , and lowering associated cost profiles . We propose a generic algorithmic building block to accelerate training of machine learning models on heterogeneous compute systems . Our scheme allows to efficiently employ compute accelerators such as GPUs and FPGAs for the training of large-scale machine learning models , when the training data exceeds their memory capacity . Resilience at the system software and at the algorithmic level is needed as a crosscutting effort . Finally , with the advent of heterogeneous compute nodes that employ standard processors as well as GPGPUs , scientific algorithms need to match these architectures to extract the most performance . This includes different system-specific levels of parallelism as well as co-scheduling of computation . Resilience at the system software and at the algorithmic level is needed as a crosscutting effort . Finally , with the advent of heterogeneous compute nodes that employ standard processors as well as GPGPUs , scientific algorithms need to match these architectures to extract the most performance . This includes different system-specific levels of parallelism as well as co-scheduling of computation . Resilience at the system software and at the algorithmic level is needed as a crosscutting effort . Finally , with the advent of heterogeneous compute nodes that employ standard processors as well as GPGPUs , scientific algorithms need to match these architectures to extract the most performance . This includes different system-specific levels of parallelism as well as co-scheduling of computation . To ensure that these non-smart machines are also monitored , the wise investment would be to fit devices which would help monitor them or else these are to be replaced with new/high end smart machinery . This situation demands a cost effective yet independent off-the-shelf sensor based device with easy integration and usage , which is introduced here , leveraging the capabilities of heterogeneous compute architecture , which can prevent unexpected machine failures by providing “ right information in the right time ” while eliminating “ unplanned stops ” thus bringing in absolute predictability on machine conditions Over the last decade , FPGAs have evolved into highly configurable SoCs with on-chip CPUs , domain-specific programmable accelerators , and flexible connectivity options . Recently , Xilinx introduced a new heterogeneous compute architecture , the Adaptive Compute Acceleration Platform ( ACAP ) , with significantly more flexibility and performance to address an evolving set of new applications such as machine learning . This advancement on the device side is accompanied by similar advances on higher-level programming approaches to make FPGAs and ACAPs significantly easy to use for a wide range of applications . ACAPs , allows users to customize their own domain-specific architecture ( DSA ) from these three programmable paradigms . Xilinx is introducing a revolutionary new heterogeneous compute architecture , the adaptive compute acceleration platform ( ACAP ) , which delivers the best of all three worlds-world-class vector and scalar processing elements tightly coupled to next-generation programmable logic ( PL ) , all tied together with a high-bandwidth network-on-chip ( NoC ) , which provides memory-mapped access to all three processing element types . This tightly coupled hybrid architecture allows more dramatic customization and performance increase than any one implementation alone . A Framework Enabling the Transparent and Efficient Programming of Heterogeneous Compute Networks . We propose PaTraCo ( Parallel Transparent Computation ) , a framework for developing parallel applications for single host or ad-hoc compute network environments incorporating a multitude of different kinds of compute devices including graphics cards . The Virtual Notebook Environment ( ViNE ) is a platform-independent , web-based interface designed to support a range of scientific activities across distributed , heterogeneous computing platforms . ViNE provides scientists with a web-based version of the common paper-based lab notebook , but in addition , it provides support for collaboration and management of computational experiments . In this paper , we propose an approach to molecular simulation-based experiments which combines workflow management and grid computing techniques to address both the computing issue due to the challenging computation requirement from molecular simulation and the management issue due to distributed , heterogeneous computing platforms . We present a workflow management system customized for such experiments and explain how this workflow system can be integrated with computational grids .
Things span , for instance , RFID tags , sensors , computers , plants , lamps , autonomous robots , and self-driving vehicles . Often , things are connected through heterogeneous platforms also providing support for , e.g. , data collection , management , and applications deployment . Additionally , things can offer their functionalities as ( web ) services facilitating their dynamic interaction .
Heterogeneous platforms are now becoming increasing omnipresent due to the availability of multicores at commodity prices . In order to benefit from the immense parallel capability of these multicores , more and more applications are now being developed as well as ported using the CUDA framework that programs these cores .
In this paper we analyze the limitations of collective MPI communication for the computation of fast Fourier transforms ( FFTs ) , which are relied on heavily for large-scale particle simulations . We present experiments made at one of the largest heterogeneous platforms , the Summit supercomputer at ORNL . We discuss communication models from state-of-the-art FFT libraries , and propose a new FFT library , named HEFFTE ( Highly Efficient FFTs for Exascale ) , which supports heterogeneous architectures and yields considerable speedups compared with CPU libraries , while maintaining good weak as well as strong scalability .
The Heterogeneous System Architecture is established by the HSA Foundation to address many of the current shortcomings at a system architecture and programming model level while providing a great foundation for already established SW models , and in addition to the GPU allow extending the architecture to other specialty processors like DSPs , FPGAs and others to interoperate within the SW framework , a main task for the next level of work in the HSA Foundation . The HSA Foundation , a not-for-profit consortium of SOC and SOC IP vendors , OEMs , academia , OSVs and ISVs defining a consistent heterogeneous platform architecture to make it dramatically easier to program heterogeneous parallel devices like GPUs and other accelerators . The presentation gives the audience a high-level understanding of the goals of HSA , the HSA system architecture properties and its use models by system software , tools and applications .
We present a framework ( real-time calculus ) for analysing various system properties pertaining to timing analysis , loads on various components and on-chip buffer memory requirements of heterogeneous platform-based architectures , in a single coherent way . Many previous analysis techniques from the real-time systems domain , which are based on standard event models , turn out to be special cases of our framework .
Especially if programmable CPUs are combined with reconfigurable logic like it has been done in the embedded domain , the complexity to design an energy efficient and powerful system increases . Therefore , heterogeneous system platforms have to be focused even stronger in research and education . Because the design and programming of these architectures is much more complex than using standard processor solutions , it is essential to provide thorough education programs for students .
Furthermore , the architecture also provides a mixed flavor of homogeneous general-purpose processing and heterogeneous reconfigurable computing . Prior approaches have mostly considered homogeneous and heterogeneous platforms as two different design paradigms despite both show domain-specific performance advantages over each other . In this context , the proposed architecture is designed for nine NoC nodes , arranged in a topology of three rows and three columns .
As part of the Oak Ridge Leadership Computing Facility (OLCF)-3 project , the Oak Ridge National Laboratory ( ORNL ) is working with several vendors and engaged in research to develop a Programming Environment ( PE ) for mixed CPU/GPU based ultra-scale computer systems . The environment provides a toolset to port or develop CPU/GPU systems while reducing development time to improve the performance and portability of the codes while minimizing sources of errors . Our toolset consists of compilers for high-level GPU directives , libraries , performance tools , and a debugger with synergistic interfaces among them .
Fourth , at each iteration of the hybrid parallel strategy , the solution vectors of individuals are transferred to the GPU and their costs are transferred back to the CPU . In order to further improve the efficiency of proposed algorithm , we propose an asynchronous transfer pattern ( stream concurrency pattern ) for CPU-GPU , in which the transfer process and computation process are overlapped and eventually the overall run-time can be reduced further . Finally , the experiments show that the solution quality obtained by our method is competitive with existing heuristic methods in reasonable time .
In the PC technology , CPU and GPU is always complement each other , in the two have developed to the emergence of new bottlenecks , the"integration"is probably the sensible solution , but on the integration of the program on the CPU and the GPU has been talked about by people . This paper studies the CPU + GPU , heterogeneous computing algorithms advantages and the possibility of future applications , especially with the general-purpose computer program interface(OpenCL)release , CPU + GPU , heterogeneous computing that looks like a CPU and the emergence of a mixture of GPU I believe this will allow a computer processor in turn to a new level , the achievements of this isomerization is more high-performance , A more cost-effective processor , which will surely mark the climax of the revolutionary GPU and CPU .
Specifically , we analyse the bottlenecks in automated and autonomous vehicles and find the on-car computer is the key component for low latency and low power self-driving vehicles . We show detailed research developments on the most advanced CPU-GPU platform including four aspects , mainly putting focus on the methods to reduce latency and energy in the system . The four aspects cover reducing data transferring overhead , reducing power consumption with power-gating , optimizing warp scheduling scheme and optimizing cache performance in coprocessors .
Therefore , throughout this research , we proposed a CPU-GPU architecture for ANC algorithms , which does not suffer from the problems of frequency domain algorithms and take advantage of GPU computation . Also , we suggested important factors needed to be concerned when an ANC algorithm based on CPU-GPU architecture is being developed . The feasibility of the proposed CPU-GPU architecture for ANC algorithms is shown with an example of implementing the proposed CPU-GPU architecture on multichannel delayless subband ANC algorithm .
In the previous research , as a way of overcoming the lack of computational power , CPU-GPU architecture was proposed so that ANC algorithms utilize the massive computing power of GPU without suffering from the block data transfer between CPU and GPU memories . However , for the feasibility test of the proposed CPU-GPU architecture in the previous research , a conventional block ANC algorithm was used , and ANC algorithm which can fully utilize the massive computing power of GPU has not been developed . In this article , ANC algorithm , which directly derives blockwise least square solution through GPU computation while generating control signals through CPU computation , is proposed .
Using detailed thermal and power maps , we demonstrate that in contrast to traditional multi-core CPUs heterogeneous processors exhibit higher intertwined behavior for dynamic voltage and frequency scaling ( DVFS ) and workload scheduling , in terms of their effect on performance , power and temperature . Second , we propose a framework to map workloads on appropriate device of CPU-GPU processors under different static and time-varying workload/system conditions . We implement the scheduler on a real CPU-GPU processor , and using OpenCL benchmarks , we demonstrate up to 24 % runtime improvement and 10 % energy savings compared to the state-of-the-art scheduling techniques .
Modern CPU-GPU processors enable workloads to run on both CPU and GPU devices . Current scheduling practices mainly use the characteristics of kernel workloads to decide the CPU/GPU mapping .
The experimental result shows that our approach achieved an overall 48.84-fold speedup ratio in ZY-3 nadir image preprocessing ( the corresponding run time is 11.60 s for one image ) , which is capable of meeting the requirement of near real-time response to the applications that follow . In addition , with the supportability of IEEE 754–2008 floating-point standard in the Fermi type GPU , preprocessing ZY-3 images with our CPU/GPU processors could maintain the quality of image preprocess as done traditionally with CPU processors .
NVIDIA Compute Unified Device Architecture (CUDA)-assisted central processing unit ( CPU ) and graphics processing unit ( GPU ) computing platform has proven its potential to be used for HPC supports . In CPU/GPU computing , original data and instructions are copied from CPU-main-memory to GPU-global-memory . Inside GPU , it would be beneficial to keep the data into shared memory ( shared only by the threads of that block ) than in the global memory ( shared by all threads ) .
Our approach , on the other hand , uses active learning to carefully select the more informative examples , thus reducing the training time . We demonstrate this technique by automatically creating a model to determine on which device to execute four parallel programs at differing problem dimensions for a representative Cpu – Gpu based system . Our methodology is remarkably simple and yet effective , making it a strong candidate for wide adoption .
Quasar consists of a high-level language , a corresponding integrated development environment ( IDE ) , a compiler , and a run-time system . Its aim is to relieve the programmer from hardware-related implementation issues that commonly occur in CPU/GPU programming . Examples include selecting memory types , memory management , concurrent kernel/function execution , hardware texturing units , etc .
In this demonstration , we present a new programming framework , Quasar , for heterogeneous programming on CPU and single/multi-GPU . Our programming framework consists of a high-level language that is aimed at relieving the programmer from hardware-related implementation issues that commonly occur in CPU/GPU programming , allowing the programmer to focus on the specification , the design , testing and the improvement of the algorithms . We will demonstrate a real-time multi-camera processing application using our integrated development environment ( IDE ) .
We evaluated this control on an Intel Baytrail-based Android platform . Experimental evaluations show energy savings of 17.4 % in the CPU-GPU subsystem with a low performance impact of 0.9 % .
Furthermore , Exascale computing is characterised by a " democratisation " of HPC : to fully exploit the capabilities of Exascale-level facilities , HPC is moving towards enabling access to its resources to a wider range of new players , including SMEs , through cloud-based approaches [ 1 ] . Finally , the need for much higher energy efficiency is pushing towards deep heterogeneity , widening the range of options for acceleration , moving from the traditional CPU-only organization , to the CPU plus GPU which currently dominates the Green5001 , to more complex options including programmable accelerators and even ( reconfigurable ) hardware accelerators [ 2 ] .
For example , in our real system we need to predict hundreds of ads for each visitor in less than 10 milliseconds . In our practice , several important techniques are deployed for accelerating online serving of industrial deep networks under the CPU - GPU architecture : i ) request batching which merges adjacent requests from CPU to take advantage of GPU power , ii ) GPU memory optimization which improves the access pattern to reduce wasted transactions in GPU memory , iii ) concurrent kernel computation which allows execution of matrix computations to be processed with multiple CUDA kernels concurrently .
Heterogeneous computing platforms consisting of general purpose processors ( GPPs ) and graphics processing units ( GPUs ) have become commonplace in personal mobile devices and embedded systems . For years , programming of these platforms was very tedious and simultaneous use of all available GPP and GPU resources required low-level programming to ensure efficient synchronization and data transfer between processors .
Abstract Despite maturing in many ways , heterogeneous distributed computing platforms continue to require substantial effort in terms of software installation and management for efficient use , often necessitating manual intervention by resource providers and end-users . In this paper we propose a novel model of resource sharing that is a viable alternative to that commonly adopted in the grid community .
A Performance Opportunity . The class of modern datacenters recently coined as “ warehouse scale computers ” ( WSCs ) has traditionally been embraced as homogeneous computing platforms . However , due to frequent machine replacements and upgrades , modern WSCs are in fact composed of diverse commodity microarchitectures and machine configurations .
The importance of high level management of complex information systems , extending over several heterogeneous and geographically distributed computing platforms , is rapidly increasing . The design of a comprehensive and enterprise-wide security strategy , including the efficient administration of large user groups while maintaining the access rights of tens of thousands of users , is next to impossible without support from sophisticated and powerful software toolsets .
Sensor networks are being applied in several emerging sophisticated applications due to the use of powerful and high-quality sensor nodes , such as radars and visible light cameras . However , these nodes need additional features to optimally benefit from heterogeneous modern computing platforms . Therefore , reconfigurable computing is a potential paradigm for those scenarios as it can provide flexibility to explore the computational resources on that kind of high performance computing system .
Abstract The PVM system , a software framework for heterogeneous concurrent computing in networked environments , has evolved in the past several years into a viable technology for distributed and parallel processing in a variety of disciplines . PVM supports a straightforward but functionally complete message passing model , and is capable of harnessing the combined resources of typically heterogeneous networked computing platforms to deliver high levels of performance and functionality . In this paper , we describe the architecture of PVM system , and discuss its computing model , the programming interface it supports , auxiliary facilities for process groups and MPP support , and some of the internal implementation techniques employed .
Being a well-accepted protocol in the industry , the proposed solution integrates existing modules that use the RS232 interface without having to redesign the whole communication system . Implementation platforms used for testing solution include Xilinx reconfigurable platforms , namely Spartan3 and Virtex FPGAs , as well as low cost microcontrollers , namely Microchip PIC18F4620 , and general purpose PCs ; industrial PCs , PLCs or other platforms with an RS-232 serial port can be easily integrated . A ring topology was selected to allow greater flexibility .
We have implemented our architecture in FPGAs and ASICs . In FPGAs platform , the new architecture increases the throughput by 45 % , compared with the latest work , and particularly the new architecture also saves nearly 12 % of hardware resources . In the 65 nm ASIC technology , the throughput of the new design can up to 80Gbps , which is 2.7 times faster than the fastest one in the literature , in particular , it also saves at least 40 % of hardware resources .
Two case studies , involving modern FPGA and floating-point applications , are used to illustrate our approach . Compared with VPR5.0 and VPR6.0 , the architecture file format adopted by Hydrate is often much smaller and easier to understand , and over 90 % of file size reduction for complex FPGAs can be achieved . Moreover , Hydrate is compatible with different versions of VPR , so that the powerful VPR tool flow can be used to explore future architectures .
However , suitable hardware has begun to appear . Complex FPGAs now provide an adequate level of speed , density and programmability in the form of reconfigurable computers , boards , and chips with embedded computational support . Such hardware could allow rapid implementation and change of systolic algorithms leading to inexpensive " programmable " systolic array hardware .
Results of the AGC algorithms on the IR images obtained by the 3 - 5 micron hand held thermal imager are discussed . The simulation results calls for hardware implementation using Xilinx SPARTAN-II FPGAs . The uniqueness of this paper lies in two features : one is the improvement suggested on the adaptive plateau equalization algorithm and the other is the hardware implementation approach .
In this paper , we propose a target board architecture suitable for embedded signal processing applications based on hardware software codesign . The target board , which serves as a system attached to a host PC via a PCI bus interface , contains a TMS320C30 DSP processor and up to four Xilinx XC5204 FPGAs . The software and hardware sections of the codesign can be easily implemented using C and VHDL programming in the C30 processor and FPGAs , respectively .
Internally , a pipeline of hardware modules for a domain-specific application is implemented in the FPGAs , where they are directly connected through their serial transceiver links . The necessary inter-FPGA communication subsystem with a flow control mechanism is implemented with Intel Arria 10 FPGAs , where the resource consumption and sustained network throughput are obtained , which averages at 7.92 GB/s . Performance estimation of a fluid simulation using the measured inter-FPGA network parameters is shown for a varied pipeline depth with explored temporal and spatial parallel options .
In this paper we propose a hardware architecture of the Fuzzy ART 's generalization applied to the classification of multi-spectral image acquired by the Algerian satellite ALSAT 1 . The developed solution is original and exploits the maximum benefit of the embedded resources in the reconfigurable circuits FPGAs . The goal is the research of a scalable architecture which gives a very good quality of classification and a very weak computing time ( about a microsecond ) with a very limited occupation of the logical resources .
A prototype of the compressor is available in software , but it runs at a speed that does not meet spacecraft requirements . The hardware implementation targets the Xilinx Virtex IV FPGAs , and makes the use of this compressor practical for Earth satellites as well as beyond-Earth missions with hyperspectral instruments .
Our research investigates how a commercial FPGA ’s fixed interconnect and CAD flow constrain the performance of NoCs based on a set of design parameters . We develop an analytical model that predicts the performance for both homogeneous and heterogeneous NoCs with a geometric mean error of 4.68 % for Xilinx Virtex 2 Pro , Virtex 4 , Virtex 5 , and Virtex 6 FPGAs , and with a geometric mean error of 5.12 % for Altera Stratix III and Stratix IV FPGAs .
However , the recent advances in InfiniBand fabrics have changed this trend . In this paper we analyze how the high bandwidth provided by the new EDR 100 G InfiniBand fabric allows remote GPU virtualization middleware systems not only to perform very similar to local GPUs , but also to improve overall performance for some applications .
For instance , the use of remote GPUs allows non-MPI applications to use a number of GPUs larger than those that can fit into a single server box . In this way , non-distributed applications may leverage all the GPUs present in the cluster , thus boosting their performance beyond the limits that the use of local GPUs may report [ 1 ] . On the other hand , the remote GPU virtualization mechanism easily allows a cluster that does not include GPUs to be extended with GPUs just by attaching one or several GPU-based servers to the cluster .
Following the MPEG CDVS standard , this paper presents an efficient feature computation phase , completely implemented on embedded devices supporting the OpenCL framework . Following our contribution to the MPEG-CDVS standard , we present the new born CDVS detector and its design for multi- core parallel GPUs . We show how to adjust algorithmic choices and implementation details to target the intrinsic characteristics of the embedded platforms selected .
Nvidia 's Tesla C2050 , codenamed Fermi , and AMD 's Radeon 5870 are two devices positioned to meet the computationally demanding needs of supercomputing research groups across the globe . Though Nvidia GPUs powered by CUDA have been the frequent choices of the performance centric research groups , the introduction and growth of OpenCL has promoted AMD GP-GPUs as potential accelerator candidates that can challenge Nvidia 's stronghold . These architectures not only offer a plethora of features for application developers to explore , but their radically different architectures calls for a detailed study that weighs their merits and evaluates their potential to accelerate complex scientific applications .
Graphics Processing Units ( GPU ) have recently been the subject of attention in research as an efficient coprocessor for implementing many classes of highly parallel applications . The GPUs design is engineered for graphics applications , where many independent SIMD workloads are simultaneously dispatched to processing elements . While parallelism has been explored in the context of traditional CPU threads and SIMD processing elements , the principles involved in dividing the steps of a parallel algorithm for execution on GPU architectures remains a significant challenge .
Wide SIMD-based GPUs have evolved into a promising platform for running general purpose workloads . Current programmable GPUs allow even code with irregular control to execute well on their SIMD pipelines .
This network effectively solved the imbalance problem between foreground and background voxels by proposing a loss function based on the Dice similarity metric . In this work , we extend the depth of the original V-Net to obtain better features to model the increased complexity of multi-class segmentation tasks at higher input/output resolutions using modern large-memory GPUs . Furthermore , we markedly improved the training behaviour of V-Net by employing batch normalization layers throughout the network .
Biomedical application speed requirements have made general purpose graphics processing unit ( GPU ) acceleration of Biomedical and Bioinformatics a lucrative area of study . SIMD design of GPUs means that most Biomedical applications are either not suitable for acceleration on GPUs , or can not reach their full acceleration potential due to thread divergence . Also , many Biomedical applications are decision heavy and experience intense thread divergence on GPUs .
Learn the history and capabilities of GPU‐based computing systems 2 . Learn the hardware organization of GPUs 3 . Learn tools and procedures for programming GPUs
We have been witnessing a continuous growth of both heterogeneous computational platforms ( e.g. , Cell blades , or the joint use of traditional CPUs and GPUs ) and multi- core processor architecture ; and it is still an open question how applications can fully exploit such computational potential efficiently . In this paper we introduce a run-time environment and programming framework which supports the implementation of scalable and efficient parallel applications in such heterogeneous , distributed environments .
Unfortunately , access to domain experts at the same physical location is not always possible and new tools and techniques are required to facilitate simultaneous and collaborative exploration of data between spatially separated experts . This paper presents a framework for collaborative visualization of biomedical data-sets , supporting heterogeneous computational platforms and network configurations . The system provides the user with data visualization , annotation and the middleware to exchange the resulting visuals between all participants , in real-time .
In this paper we present a workflow solution to support graphically the design , execution , monitoring , and performance visualisation of complex grid applications . The described workflow concept can provide interoperability among different types of legacy applications on heterogeneous computational platforms , such as Condor or Globus based grids . The major design and implementation issues concerning the integration of Condor tools , Mercury grid monitoring infrastructure , PROVE performance visualisation tool , and the new workflow layer of P-GRADE are discussed in two scenarios .
Our initial experiments indicate that an 8 node heterogeneous cluster can realize over 100times speedup compared to a high performance uniprocessor system . By creating a platform based on modern hardware , we believe that a heterogeneous compute platform customized for image registration can provide robust , scalable , cost effective sub-minute medical image registration capabilities .
Traditional hardware evelopers can still port their existing RTL to ACAP via the traditional RTL entry flow . This talk reviews the needs driving the change from the traditional CPU-based compute model , explores the alternative options , and unveils the Xilinx Versal ACAP , the industry 's first heterogeneous compute platform .
Raising the Abstraction Level . We present an overview of the evolution of programming techniques for Field-Programmable Gate Arrays ( FPGAs ) , with a particular focus on High-Level Synthesis ( HLS ) and Heterogeneous Computing ( HC ) , and we argue that , in the context of High-Performance Computing ( HPC ) , FPGAs should be treated as components of a larger heterogeneous compute platform . Consequently , HLS and HC tools become compilation targets rather than high-level development tools .
Smart cyber-physical systems ( CPSs ) have complex interaction with their environment which is rarely known in advance , and they heavily depend on intelligent data processing carried out over a heterogeneous and distributed computation platform with resource-constrained devices to monitor , manage and control autonomous behavior . First , we propose a distributed runtime model to capture the operational state and the context information of a smart CPS using directed , typed and attributed graphs as high-level knowledge representation .
Bioinformatics workloads are characterized by huge data sets and complex algorithms , requiring enormous data processing and making high performance heterogeneous computation platforms such as FPGAs and GPUs highly relevant . We compare three accelerated implementations of the widely used BWA-MEM genomic mapping tool as a case study on design-time optimization for heterogeneous architectures : BWA-MEM-CUDA , BWA-MEM-OpenCL , and BWA-MEMVHDL , each using an optimized Smith-Waterman algorithm implementation .
For this reason we propose a flow , which assists the software developer at design time , offering immediate power- and performance-estimation . Such approaches are already known in the embedded world , helping there to select between different design possibilities , and will be used to get the best possible performance from a massively heterogeneous computation platform , while still keeping the energy consumption in mind .
To address this question , an open-source neuromorphic processor architecture simulator called NeMo is being developed . This effort will enable the design space exploration of potential heterogeneous compute systems that combine traditional CPUs , GPUs , and neuromorphic hardware . This article examines the design , implementation , and performance of NeMo .
Significant application performance improvements can be achieved by heterogeneous compute technologies , such as multi-core CPUs , GPUs and FPGAs . The HARNESS project is developing architectural principles that enable the next generation cloud platforms to incorporate such devices thereby vastly increasing performance , reducing energy consumption , and lowering associated cost profiles .
We propose a generic algorithmic building block to accelerate training of machine learning models on heterogeneous compute systems . Our scheme allows to efficiently employ compute accelerators such as GPUs and FPGAs for the training of large-scale machine learning models , when the training data exceeds their memory capacity .
Resilience at the system software and at the algorithmic level is needed as a crosscutting effort . Finally , with the advent of heterogeneous compute nodes that employ standard processors as well as GPGPUs , scientific algorithms need to match these architectures to extract the most performance . This includes different system-specific levels of parallelism as well as co-scheduling of computation .
Resilience at the system software and at the algorithmic level is needed as a crosscutting effort . Finally , with the advent of heterogeneous compute nodes that employ standard processors as well as GPGPUs , scientific algorithms need to match these architectures to extract the most performance . This includes different system-specific levels of parallelism as well as co-scheduling of computation .
Resilience at the system software and at the algorithmic level is needed as a crosscutting effort . Finally , with the advent of heterogeneous compute nodes that employ standard processors as well as GPGPUs , scientific algorithms need to match these architectures to extract the most performance . This includes different system-specific levels of parallelism as well as co-scheduling of computation .
To ensure that these non-smart machines are also monitored , the wise investment would be to fit devices which would help monitor them or else these are to be replaced with new/high end smart machinery . This situation demands a cost effective yet independent off-the-shelf sensor based device with easy integration and usage , which is introduced here , leveraging the capabilities of heterogeneous compute architecture , which can prevent unexpected machine failures by providing “ right information in the right time ” while eliminating “ unplanned stops ” thus bringing in absolute predictability on machine conditions
Over the last decade , FPGAs have evolved into highly configurable SoCs with on-chip CPUs , domain-specific programmable accelerators , and flexible connectivity options . Recently , Xilinx introduced a new heterogeneous compute architecture , the Adaptive Compute Acceleration Platform ( ACAP ) , with significantly more flexibility and performance to address an evolving set of new applications such as machine learning . This advancement on the device side is accompanied by similar advances on higher-level programming approaches to make FPGAs and ACAPs significantly easy to use for a wide range of applications .
ACAPs , allows users to customize their own domain-specific architecture ( DSA ) from these three programmable paradigms . Xilinx is introducing a revolutionary new heterogeneous compute architecture , the adaptive compute acceleration platform ( ACAP ) , which delivers the best of all three worlds-world-class vector and scalar processing elements tightly coupled to next-generation programmable logic ( PL ) , all tied together with a high-bandwidth network-on-chip ( NoC ) , which provides memory-mapped access to all three processing element types . This tightly coupled hybrid architecture allows more dramatic customization and performance increase than any one implementation alone .
A Framework Enabling the Transparent and Efficient Programming of Heterogeneous Compute Networks . We propose PaTraCo ( Parallel Transparent Computation ) , a framework for developing parallel applications for single host or ad-hoc compute network environments incorporating a multitude of different kinds of compute devices including graphics cards .
The Virtual Notebook Environment ( ViNE ) is a platform-independent , web-based interface designed to support a range of scientific activities across distributed , heterogeneous computing platforms . ViNE provides scientists with a web-based version of the common paper-based lab notebook , but in addition , it provides support for collaboration and management of computational experiments .
In this paper , we propose an approach to molecular simulation-based experiments which combines workflow management and grid computing techniques to address both the computing issue due to the challenging computation requirement from molecular simulation and the management issue due to distributed , heterogeneous computing platforms . We present a workflow management system customized for such experiments and explain how this workflow system can be integrated with computational grids .
Message from the IoT-ASAP Chairs Review of LLVM Compiler Architecture Enhancements for CUDA Impacts of Multi-GPU MPI Collective Communications on Large FFT Computation The Heterogeneous System Architecture: It's beyond the GPU A general framework for analysing system properties in platform-based embedded system designs Comprehensive curriculum for reconfigurable heterogeneous computer architecture education Design of a hybrid multicore platform for high performance reconfigurable computing A Programming Environment for Heterogeneous Multi-Core Computer Systems A Parallel Genetic Algorithm With Dispersion Correction for HW/SW Partitioning on Multi-Core CPU and Many-Core GPU On the CPU + GPU Heterogeneous Computing Research and Analysis Optimization Methods for Computing System in Mobile CPS CPU-GPU architecture for active noise control Blockwise Weighted Least Square Active Noise Control for CPU-GPU Architecture New Techniques for Power-Efficient CPU-GPU Processors Power-aware characterization and mapping of workloads on CPU-GPU processors CPU/GPU near real-time preprocessing for ZY-3 satellite images: Relative radiometric correction, MTF compensation, and geocorrection A talented CPU-to-GPU memory mapping technique Intelligent Heuristic Construction with Active Learning Real-time Depth Estimation and View Interpolation using Quasar Quasar - a new programming framework for real-time image/video processing on GPU and CPU A control-theoretic approach for energy efficient CPU-GPU subsystem in mobile platforms Reliable power and time-constraints-aware predictive management of heterogeneous exascale systems Deep Interest Network for Click-Through Rate Prediction Executing Dynamic Data Rate Actor Networks on OpenCL Platforms Unibus: a contrarian approach to grid computing Heterogeneity in “Homogeneous” Warehouse-Scale Computers: A Performance Opportunity Security management for administration and control of corporate-wide, diverse systems Dynamic Reconfigurable Task Schedule Support towards a Reflective Middleware for Sensor Network The PVM Concurrent Computing System: Evolution, Experiences, and Trends Intra- and inter-circuit network for Petri nets based components HPAZ: A high-throughput pipeline architecture of ZUC in hardware Hydrate: Hybrid Reconfigurable Architecture Expressions Automatic latency-optimal design of FPGA-based systolic arrays Algorithms and Hardware Implementation of Real Time Automatic Gain Control Feature for Thermal Imager Rapid Prototyping of Hardware / Software Codesign for Embedded Signal Processing Performance Estimation of Deeply Pipelined Fluid Simulation on Multiple FPGAs with High-speed Communication Subsystem Implementation of fuzzy-ART on FPGA: Classification of the Algiers's Bay Fast and Adaptive Lossless Onboard Hyperspectral Data Compression System An analytical model describing the performance of application-specific networks-on-chip on field-programmable gate arrays Local and Remote GPUs Perform Similar with EDR 100G InfiniBand A Live Demo on Remote GPU Accelerated Deep Learning Using the rCUDA Middleware Accurate and Efficient Visual Search on Embedded Systems Evaluation of GPU Architectures Using Spiking Neural Networks A Parallel Implementation of K-Means Clustering on GPUs CAPRI: Prediction of compaction-adequacy for handling control-divergence in GPGPU architectures Improving V-Nets for multi-class abdominal organ segmentation Comprehensive Branch Elimination for GPU Accelerated Biomedical Applications TH‐C‐BRA‐01: An introduction to GPU Computing Exploiting Computational Resources in Distributed Heterogeneous Platforms Distributed and Collaborative Biomedical Data Exploration Workflow Support for Complex Grid Applications: Integrated and Portal Solutions Towards a Heterogeneous Medical Image Registration Acceleration Platform Advances in Adaptable Computing FPGAs as Components in Heterogeneous High-Performance Computing Systems: Raising the Abstraction Level Distributed graph queries over models@run.time for runtime monitoring of cyber-physical systems Comparative Analysis of System-Level Acceleration Techniques in Bioinformatics: A Case Study of Accelerating the Smith-Waterman Algorithm for BWA-MEM Using Early Power and Timing Estimations of Massively Heterogeneous Computation Platforms to Create Optimized HPC Applications NeMo: A Massively Parallel Discrete-Event Simulation Model for Neuromorphic Architectures Cross Resource Optimisation of Database Functionality across Heterogeneous Processors Efficient Use of Limited-Memory Accelerators for Linear Learning on Heterogeneous Systems Proceedings of the second workshop on Scalable algorithms for large-scale systems Proceedings of the 5th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems, ScalA '14, New Orleans, Louisiana, USA, November 16-21, 2014 Proceedings of the 6th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems, ScalA@SC 2015, Austin, Texas, USA, November 15, 2015 A Novel Sensor Based Approach to Predictive Maintenance of Machines by Leveraging Heterogeneous Computing Xilinx Vitis Unified Software Platform Advances in Adaptable Computing PaTraCo: A Framework Enabling the Transparent and Efficient Programming of Heterogeneous Compute Networks A Prototype Notebook-Based Environment for Computational Tools Computational Tools A Workflow Management and Grid Computing Approach to Molecular Simulation-Based Bio/Nano Experiments
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ICSA Workshops ICSAMOS DATE NORCAS IISWC Computational Imaging ICDSC DAC SAMOS KDD SiPS ISPA DATE FPT FCCM ICVGIP ASAP CIST Middleware Industry Middleware Posters and Demos PDPTA ISCA Medical Imaging: Image Processing SBAC-PAD ISVC European Across Grids Conference ISPD PARCO BIBE EUC ISPA NIPS FPGA ISPD EGPGV@Eurographics International Conference on Computational Science
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