TY - GEN
T1 - CuZ-Checker
T2 - 2021 IEEE International Conference on Cluster Computing, Cluster 2021
AU - Yu, Xiaodong
AU - Di, Sheng
AU - Gok, Ali Murat
AU - Tao, Dingwen
AU - Cappello, Franck
N1 - Publisher Copyright:
©2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Lossy compression is becoming an indispensable technique for the success of today's extreme-scale highperformance computing projects that produce vast volumes of data during scientific simulations or instrument data acquisitions. Comprehensively understanding the compression quality and performance of different lossy compressors is critical to selecting the best-fit compressors and using them properly and efficiently in practice. A few lossy compression assessment tools (e.g., Z-checker) have been developed, but none of them support the execution in a GPU environment. This is a significant gap because many recent extreme-scale applications and lossy compressors (e.g., cuSZ) can run entirely within GPUs. In this work, we develop an efficient lossy compression measuring system (called cuZ-Checker) on the GPU platform, which aims to perform the lossy compression quality and performance assessment completely within the GPU environment. Our contribution is threefold. (1) We develop a novel GPU-based lossy compression measuring framework using a computation pattern-based design approach. This approach classifies the computing-intensive metrics into three categories based on their patterns which creates large opportunities for kernel fusion and data reuse. (2) For each pattern in cuZ-Checker, we develop a CUDA kernel and provide fine-grained optimizations to boost its performance. (3) We thoroughly evaluate our cuZ-checker on a V100 GPU using four real-world scientific application datasets. Experiments show that cuZ-Checker can significantly accelerate the overall lossy compression assessment performance by 23X~31X compared with the OpenMP-based multithreading CPU performance. To the best of our knowledge, this is the first lossy compression measuring system designed for GPU devices.
AB - Lossy compression is becoming an indispensable technique for the success of today's extreme-scale highperformance computing projects that produce vast volumes of data during scientific simulations or instrument data acquisitions. Comprehensively understanding the compression quality and performance of different lossy compressors is critical to selecting the best-fit compressors and using them properly and efficiently in practice. A few lossy compression assessment tools (e.g., Z-checker) have been developed, but none of them support the execution in a GPU environment. This is a significant gap because many recent extreme-scale applications and lossy compressors (e.g., cuSZ) can run entirely within GPUs. In this work, we develop an efficient lossy compression measuring system (called cuZ-Checker) on the GPU platform, which aims to perform the lossy compression quality and performance assessment completely within the GPU environment. Our contribution is threefold. (1) We develop a novel GPU-based lossy compression measuring framework using a computation pattern-based design approach. This approach classifies the computing-intensive metrics into three categories based on their patterns which creates large opportunities for kernel fusion and data reuse. (2) For each pattern in cuZ-Checker, we develop a CUDA kernel and provide fine-grained optimizations to boost its performance. (3) We thoroughly evaluate our cuZ-checker on a V100 GPU using four real-world scientific application datasets. Experiments show that cuZ-Checker can significantly accelerate the overall lossy compression assessment performance by 23X~31X compared with the OpenMP-based multithreading CPU performance. To the best of our knowledge, this is the first lossy compression measuring system designed for GPU devices.
KW - GPU
KW - Lossy compression
KW - Performance optimization
KW - Quality evaluation
KW - SSIM
UR - http://www.scopus.com/inward/record.url?scp=85126017370&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85126017370&partnerID=8YFLogxK
U2 - 10.1109/Cluster48925.2021.00065
DO - 10.1109/Cluster48925.2021.00065
M3 - Conference contribution
AN - SCOPUS:85126017370
T3 - Proceedings - IEEE International Conference on Cluster Computing, ICCC
SP - 307
EP - 319
BT - Proceedings - 2021 IEEE International Conference on Cluster Computing, Cluster 2021
Y2 - 7 September 2021 through 10 September 2021
ER -