Code generation and adaptive control divergence management for light weight SIMT processors
The energy costs of data movement are limiting the performance scaling of future generations of high performance computing architectures targeted to data intensive applications. The result has been a resurgence in the interest in processing-in-memory (PIM) architectures. This challenge has spawned the development of a scalable, parametric data parallel architecture referred at the Heterogeneous Architecture Research Prototype (HARP) - a single instruction multiple thread (SIMT) architecture for integration into DRAM systems, particularly 3D memory stacks as a distinct processing layer to exploit the enormous internal memory bandwidth. However, this potential can only be realized with an optimizing compilation environment. This thesis addresses this challenge by i) the construction of an open source compiler for HARP, and ii) integrating optimizations for handling control flow divergence for HARP instances. The HARP compiler is built using the LLVM open source compiler infrastructure. Apart from traditional code generation, the HARP compiler backend handles unique challenges associated with the HARP instruction set. Chief among them is code generation for control divergence management techniques. The HARP architecture and compiler supports i) a hardware reconvergence stack and ii) predication to handle divergent branches. The HARP compiler addresses several challenges associated with generating code for these two control divergence management techniques and implements multiple analyses and transformations for code generation. Both of these techniques have unique advantages and disadvantages depending upon whether the conditional branch is likely to be unanimous or not. Two decision frameworks, guided by static analysis and dynamic profile information are implemented to choose between the control divergence management techniques by analyzing the nature of the conditional branches and utilizing this information during compilation.