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Programming to achieve high performance for NVIDIA GPUs using CUDA has been known to be challenging. A GPU has hundreds or thousands of cores that a program must exhibit sufficient parallelism to achieve maximum GPU utilization. A system with GPU accelerators has a heterogeneous and deep memory system that programmers must effectively and correctly use to fully take advantage of the GPU's parallelism capability. In this paper, we present CUDAMicroBench, a collection of fourteen microbenchmarks that demonstrate performance challenges in CUDA programming and techniques to optimize the CUDA programs to address these challenges. It also includes examples and techniques for using advanced CUDA features such as data shuffling between threads, dynamic parallelism, etc that can help users optimize the CUDA program for performance. The microbenchmark can be used for evaluating the performance of GPU architectures, the memory systems of GPU itself and of the whole system architectures, and for evaluating the effectiveness of compiler and performance tools for performance analysis. It can be used to help users understand the complexity of heterogeneous GPU-accelerator systems through examples and guide users for performance optimization. It is released as BSD-licensed open-source from https://github.com/passlab/CUDAMicroBench.git.more » « less
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HOMP: Automated Distribution of Parallel Loops and Data in Highly Parallel Accelerator-Based SystemsYan, Yonghong ; Liu, Jiawen ; Cameron, Kirk W. ; Umar, Mariam ( , Parallel and Distributed Processing Symposium (IPDPS), 2017 IEEE International)Heterogeneous computing systems, e.g., those with accelerators than the host CPUs, offer the accelerated performance for a variety of workloads. However, most parallel programming models require platform dependent, time-consuming hand-tuning efforts for collectively using all the resources in a system to achieve efficient results. In this work, we explore the use of OpenMP parallel language extensions to empower users with the ability to design applications that automatically and simultaneously leverage CPUs and accelerators to further optimize use of available resources. We believe such automation will be key to ensuring codes adapt to increases in the number and diversity of accelerator resources for future computing systems. The proposed system combines language extensions to OpenMP, load-balancing algorithms and heuristics, and a runtime system for loop distribution across heterogeneous processing elements. We demonstrate the effectiveness of our automated approach to program on systems with multiple CPUs, GPUs, and MICs.more » « less