Tejaswi Agarwal
ABSTRACT
In the last few years, GPUs have become an integral part of HPC clusters. To test these heterogeneous CPU-GPU systems, we designed a hybrid CUDA-MPI benchmark suite that consists of three communication- and compute-intensive applications: Matrix Multiplication (MM), Needleman-Wunsch (NW) and the ADFA compression algorithm [1]. The main goal of this work is to characterize these workloads on CPU-GPU clusters. Our benchmark applications are designed to allow cluster administrators to identify bottlenecks in the cluster, to decide if scaling applications to multiple nodes would improve or decrease overall throughput and to design effective scheduling policies. Our experiments show that inter-node communication can significantly degrade the throughput of communication-intensive applications. We conclude that the scalability of the applications depends primarily on two factors: the cluster configuration and the applications characteristics.
- M. Becchi and P. Crowley, ?A-DFA: A Time- and Space- Efficient DFA Compression Algorithm for Fast Regular Expression Evaluation,? ACM TACO, vol. 10, no. 1, pp. 1--26, 2013. Google Scholar
Digital Library
- S. B. Needleman, and C. D. Wunsch, ?A general method applicable to the search for similarities in the amino acid sequence of two proteins,? J. of Molecular Biology, vol. 48,no. 3, pp. 443--453, 1970.Google Scholar
- How to Optimize Data Transfers in CUDA C/C++, http://devblogs.nvidia.com/parallelforall/how-optimize-datatransfers- cuda-cc.Google Scholar
- An Introduction to CUDA-Aware MPI, http://devblogs.nvidia.com/parallelforall/introduction-cudaaware-mpi.Google Scholar
Index Terms
- Design of a Hybrid MPI-CUDA Benchmark Suite for CPU-GPU Clusters
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