Bin Ren
ABSTRACT
Orchestrating data transfers between CPUs and a coprocessor manually is cumbersome, particularly for multi-dimensional arrays and other data structures with multi-level pointers, which are common in scientific computations. This work describes a system that includes both compile-time and runtime solutions for this problem, with the overarching goal of improving programmer productivity while maintaining performance.
We implemented our best compile-time solution, partial linearization with pointer reset, as a source-to-source transformation, and evaluated our work by multiple C benchmarks. Our experiment results demonstrate that our best compile-time solution can perform 2.5x-5x faster than original runtime solution, and the CPU-Coprocessor code with it can achieve 1.5x-2.5x speedup over the 16-thread CPU version.
- S. Lee and R. Eigenmann. OpenMPC: Extended OpenMP Programming and Tuning for GPUs. In SC, 2010. Google Scholar
Digital Library
- N. Ravi, Y. Yang, T. Bao, and S. Chakradhar. Apricot: an Optimizing Compiler and Productivity Tool for x86-Compatible Many-Core Coprocessors. In ICS, pages 47--58, 2012. Google ScholarDigital Library
Index Terms
- Automating and optimizing data transfers for many-core coprocessors
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