A recent development in radio astronomy is to replace traditional dishes with many small antennas. The signals are combined to form one large, virtual telescope. The enormous data streams are cross-correlated to filter out noise. This is especially challenging, since the computational demands grow quadratically with the number of data streams. Moreover, the correlator is not only computationally intensive, but also very I O intensive. The LOFAR telescope, for instance, will produce over 100 terabytes per day. The future SKA telescope will even require in the order of exaflops, and petabits s of I O. A recent trend is to correlate in software instead of dedicated hardware, to increase flexibility and to reduce development efforts. We evaluate the correlator algorithm on multi-core CPUs and many-core architectures, such as NVIDIA and ATI GPUs, and the Cell B.E. The correlator is a streaming, real-time application, and is much more I O intensive than applications that are typically implemented on many-core hardware today. We compare with the LOFAR production correlator on an IBM Blue Gene P supercomputer. We investigate performance, power efficiency, and programmability. We identify several important architectural problems which cause architectures to perform suboptimally. Our findings are applicable to data-intensive applications in general. The processing power and memory bandwidth of current GPUs are highly imbalanced for correlation purposes. While the production correlator on the Blue Gene P achieves a superb 96 of the theoretical peak performance, this is only 16 on ATI GPUs, and 32 on NVIDIA GPUs. The Cell B.E. processor, in contrast, achieves an excellent 92 . We found that the Cell B.E. and NVIDIA GPUs are the most energy-efficient solutions, they run the correlator at least 4 times more energy efficiently than the Blue Gene P. The research presented is an important pathfinder for next-generation telescopes.
@cite_2 describe an auto-tuning framework for multi-cores. The framework can automatically perform different low-level optimizations to increase performance. However, GPUs are not considered in this framework. We performed all optimizations manually, which is possible in our case, since the algorithm is relatively straightforward. More important, we found that in our case, algorithmic changes are required to achieve good performance. Examples include the use of different tile sizes, and vectorizing over the different polarizations instead of the inner time loop.
@cite_2
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We present an auto-tuning approach to optimize application performance on emerging multicore architectures. The methodology extends the idea of search-based performance optimizations, popular in linear algebra and FFT libraries, to application-specific computational kernels. Our work applies this strategy to Sparse Matrix Vector Multiplication (SpMV), the explicit heat equation PDE on a regular grid (Stencil), and a lattice Boltzmann application (LBMHD). We explore one of the broadest sets of multicore architectures in the HPC literature, including the Intel Xeon Clovertown, AMD Opteron Barcelona, Sun Victoria Falls, and the Sony-Toshiba-IBM (STI) Cell. Rather than hand-tuning each kernel for each system, we develop a code generator for each kernel that allows us to identify a highly optimized version for each platform, while amortizing the human programming effort. Results show that our auto-tuned kernel applications often achieve a better than 4X improvement compared with the original code. Additionally, we analyze a Roofline performance model for each platform to reveal hardware bottlenecks and software challenges for future multicore systems and applications.