Debunking the 100X GPU vs. CPU myth: an evaluation of throughput computing on CPU and GPU

Victor W. Lee,Changkyu Kim,Jatin Chhugani,Michael Deisher,Daehyun Kim,Anthony D. Nguyen,Nadathur Satish,Mikhail Smelyanskiy,Srinivas Chennupaty,Per Hammarlund,Ronak Singhal,Pradeep Dubey
Throughput Computing Lab, Intel Corporation
SIGARCH Comput. Archit. News, Vol. 38, No. 3. (2010), pp. 451-460


   title={Debunking the 100X GPU vs. CPU myth: an evaluation of throughput computing on CPU and GPU},

   author={Lee, V.W. and Kim, C. and Chhugani, J. and Deisher, M. and Kim, D. and Nguyen, A.D. and Satish, N. and Smelyanskiy, M. and Chennupaty, S. and Hammarlund, P. and others},

   booktitle={Proceedings of the 37th annual international symposium on Computer architecture},





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Recent advances in computing have led to an explosion in the amount of data being generated. Processing the ever-growing data in a timely manner has made throughput computing an important aspect for emerging applications. Our analysis of a set of important throughput computing kernels shows that there is an ample amount of parallelism in these kernels which makes them suitable for today’s multi-core CPUs and GPUs. In the past few years there have been many studies claiming GPUs deliver substantial speedups (between 10X and 1000X) over multi-core CPUs on these kernels. To understand where such large performance difference comes from, we perform a rigorous performance analysis and find that after applying optimizations appropriate for both CPUs and GPUs the performance gap between an Nvidia GTX280 processor and the Intel Core i7-960 processor narrows to only 2.5x on average. In this paper, we discuss optimization techniques for both CPU and GPU, analyze what architecture features contributed to performance differences between the two architectures, and recommend a set of architectural features which provide significant improvement in architectural efficiency for throughput kernels.
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