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2017 GTC San Jose

S7607 - Floating Point Array Compression on the GPU

Session Speakers
Session Description

To increase performance, high-performance systems are adopting a heterogeneous approach through the use of accelerators (for example, GPUs). These accelerators provide this performance increase with massive parallelization. Unfortunately, these HPC systems, with or without accelerators, are hitting a wall: an increasing divergence between compute and bandwidth. As core counts have increased and bandwidth at all levels of the system have stagnated, data movement has become the bottleneck for performance at multiple places between subsystems: storage, network, accelerator, and memory levels. To address these bandwidth issues in heterogeneous systems, we developed a lossy fixed-rated compression algorithm, cuZFP, for the GPU. The ZFP compressor specifically addresses the needs of lossy compression for high-performance floating point data like those used in scientific codes. By extending lossy compression to the GPU, the compression is up to an order of magnitude faster than the CPU version. Further, bandwidth limitations can be eased directly on the accelerator without copying the data back to the CPU.


Additional Session Information
Advanced
Talk
HPC and Supercomputing In-Situ and Scientific Visualization
Higher Education / Research
25 minutes
Session Schedule