TY - GEN
T1 - Quantifying data locality in dynamic parallelism in GPUs
AU - Tang, Xulong
AU - Pattnaik, Ashutosh
AU - Kayiran, Onur
AU - Jog, Adwait
AU - Kandemir, Mahmut
AU - Das, Chitaranjan
PY - 2019/6/20
Y1 - 2019/6/20
N2 - Dynamic parallelism (DP) is a new feature of emerging GPUs that allows new kernels to be generated and scheduled from the deviceside (GPU) without the host-side (CPU) intervention. To eiciently support DP, one of the major challenges is to saturate the GPU processing elements and provide them with the required data in a timely fashion. In this paper, we irst conduct a limit study on the performance improvements that can be achieved by hardware schedulers that are provided with accurate data reuse information. We next propose LASER, a Locality-Aware SchedulER, where the hardware schedulers employ data reuse monitors to help make scheduling decisions to improve data locality at runtime. Experimental results on 16 benchmarks show that LASER, on an average, can improve performance by 11.3%.
AB - Dynamic parallelism (DP) is a new feature of emerging GPUs that allows new kernels to be generated and scheduled from the deviceside (GPU) without the host-side (CPU) intervention. To eiciently support DP, one of the major challenges is to saturate the GPU processing elements and provide them with the required data in a timely fashion. In this paper, we irst conduct a limit study on the performance improvements that can be achieved by hardware schedulers that are provided with accurate data reuse information. We next propose LASER, a Locality-Aware SchedulER, where the hardware schedulers employ data reuse monitors to help make scheduling decisions to improve data locality at runtime. Experimental results on 16 benchmarks show that LASER, on an average, can improve performance by 11.3%.
UR - http://www.scopus.com/inward/record.url?scp=85067657792&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85067657792&partnerID=8YFLogxK
U2 - 10.1145/3309697.3331473
DO - 10.1145/3309697.3331473
M3 - Conference contribution
T3 - SIGMETRICS Performance 2019 - Abstracts of the 2019 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems
SP - 25
EP - 26
BT - SIGMETRICS Performance 2019 - Abstracts of the 2019 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems
PB - Association for Computing Machinery, Inc
T2 - 14th Joint Conference of International Conference on Measurement and Modeling of Computer Systems, SIGMETRICS 2019 and IFIP Performance Conference 2019, SIGMETRICS/Performance 2019
Y2 - 24 June 2019 through 28 June 2019
ER -