Proceedings of the 22nd International Conference on Parallel Architectures and Compilation Techniques (2011)
Galveston, Texas USA
Oct. 10, 2011 to Oct. 14, 2011
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/PACT.2011.17
State-of-the-art graphic processing units (GPUs) can offer very high computational throughput for highly parallel applications using hundreds of integrated cores. In general, the peak throughput of a GPU is proportional to the product of the number of cores and their frequency. However, the product is often limited by a power constraint. Although the throughput can be increased with more cores for some applications, it cannot for others because parallelism of applications and/or bandwidth of on-chip interconnects/caches and off-chip memory are limited. In this paper, first, we demonstrate that adjusting the number of operating cores and the voltage/frequency of cores and/or on-chip interconnects/caches for different applications can improve the throughput of GPUs under a power constraint. Second, we show that dynamically scaling the number of operating cores and the voltages/frequencies of both cores and on-chip interconnects/caches at runtime can improve the throughput of application even further. Our experimental results show that a GPU adopting our runtime dynamic voltage/frequency and core scaling technique can provide up to 38% (and nearly 20% on average) higher throughput than the baseline GPU under the same power constraint.
GPU, dynamic voltage, frequency, core scaling, power constraint, throughput
Jungseob Lee, Vijay Sathisha, Michael Schulte, Katherine Compton, Nam Sung Kim, "Improving Throughput of Power-Constrained GPUs Using Dynamic Voltage/Frequency and Core Scaling", Proceedings of the 22nd International Conference on Parallel Architectures and Compilation Techniques, vol. 00, no. , pp. 111-120, 2011, doi:10.1109/PACT.2011.17