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Redefining the Role of the CPU in the Era of CPU-GPU Integration

Manish Arora, Siddhartha Nath, Subhra Mazumdar, Scott Baden, Dean Tullsen
Department of Computer Science & Engineering, University of California, San Diego
IEEE Micro, Issue: 99, 2012
@article{arora2012redefining,

   title={Redefining the Role of the CPU in the Era of CPU-GPU Integration},

   author={Arora, M. and Nath, S. and Mazumdar, S. and Baden, S. and Tullsen, D.},

   year={2012},

   publisher={IEEE}

}

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GPU computing has emerged as a viable alternative to CPUs for throughput oriented applications or regions of code. Speedups of 10 to 100x over CPU implementations have been reported. This trend is expected to continue in the future with GPU architectural advances, improved programming support, scaling, and tighter CPU-GPU chip integration. However, not all code will get mapped to the GPUs, even for many of those applications which map well to the GPU – the CPU still runs code that is not targeted to the GPU, and often that code is still very much performance-critical. This paper demonstrates that the code that the CPU will be expected to execute in an integrated CPU-GPU environment is profoundly different than the code it has been optimized for over the past many generations. The characteristics of this new code should drive future CPU design and architecture. Specifically, this work shows that post-GPU code tends to have lower ILP, significantly more difficult to predict loads, harder to predict stores, and more difficult branch prediction. Post-GPU code exhibits smaller gains from the availability of multiple cores because of reduced thread level parallelism.
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