CEAZ: Accelerating Parallel I/O via Hardware-Algorithm Co-Designed Adaptive Lossy Compression

TL;DR

CEAZ is a hardware-algorithm co-designed lossy compression system for scientific data on FPGAs that achieves high compression ratios and throughput, significantly accelerating parallel I/O in HPC systems.

Contribution

This work introduces the first FPGA-based adaptive lossy compressor with a novel Huffman coding approach and precise ratio control, optimized for scientific data in HPC environments.

Findings

CEAZ achieves 2.3X higher throughput than previous FPGA compressors.

CEAZ attains 3.0X better compression ratio.

Parallel I/O throughput improves up to 37.8X.

Abstract

As HPC systems continue to grow to exascale, the amount of data that needs to be saved or transmitted is exploding. To this end, many previous works have studied using error-bounded lossy compressors to reduce the data size and improve the I/O performance. However, little work has been done for effectively offloading lossy compression onto FPGA-based SmartNICs to reduce the compression overhead. In this paper, we propose a hardware-algorithm codesign for an efficient and adaptive lossy compressor for scientific data on FPGAs (called CEAZ), which is the first lossy compressor that can achieve high compression ratios and throughputs simultaneously. Specifically, we propose an efficient Huffman coding approach that can adaptively update Huffman codewords online based on codewords generated offline, from a variety of representative scientific datasets. Moreover, we derive a theoretical analysis to support a precise control of compression ratio under an error-bounded compression mode, enabling accurate offline Huffman codewords generation. This also helps us create a fixed-ratio compression mode for consistent throughput. In addition, we develop an efficient compression pipeline by adopting cuSZ's dual-quantization algorithm to our hardware use cases. Finally, we evaluate CEAZ on five real-world datasets with both a single FPGA board and 128 nodes (to accelerate parallel I/O). Experiments show that CEAZ outperforms the second-best FPGA-based lossy compressor by 2.3X of throughput and 3.0X of ratio. It also improves MPI_File_write and MPI_Gather throughputs by up to 28.9X and 37.8X, respectively.