LLVM: A Compilation Framework for Lifelong Program Analysis & Transformation
Proceedings of the international symposium on Code generation and optimization: feedback-directed and runtime optimization
A translation system for enabling data mining applications on GPUs
Proceedings of the 23rd international conference on Supercomputing
Optimising purely functional GPU programs
Proceedings of the 18th ACM SIGPLAN international conference on Functional programming
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General purpose computing using GPUs is becoming increasingly popular, because of GPU's extremely favorable performance/price ratio. Like standard processors, GPUs also have a memory hierarchy, which must be carefully optimized for in order to achieve efficient execution. Specifically, modern NVIDIA GPUs have a very small programmable cache, referred to as shared memory, accesses to which are nearly 100 to 150 times faster than accesses to the regular device memory. An automatically generated or hand-written CUDA program can explicitly control what variables and array sections are allocated on the shared memory at any point during the execution. This, however, leads to a difficult optimization problem. In this paper, we formulate and solve the shared memory allocation problem as an integer linear programming problem. We present a global (intraprocedural) framework which can model structured control flow, and is not restricted to a single loop nest. We consider allocation of scalars, arrays, and array sections on shared memory. We also briefly show how our framework can suggest useful loop transformations to further improve performance. Our experiments using several non-scientific application show that our integer programming framework outperforms a recently published heuristic method, and our loop transformations also improve performance for many applications.