A Memory-Efficient and Computation-Balanced Lossy Compressor on Wafer-Scale Engine
Shihui Song, Robert Underwood, Sheng Di, Yafan Huang, Peng Ming Jiang, Franck Cappello · 2025
Cerebras system has demonstrated immense potential across various scientific domains. However, modern scientific simulations frequently generate vast volumes of data in a short time, leading to bottlenecks in runtime performance and memory footprint. While an ultra-fast error-bounded lossy compressor can mitigate such limitations with high compression ratios and guaranteed data quality, deploying it into Cerebras dataflow architecture poses significant difficulties. Specifically, Cerebras faces memory challenges, such as the absence of shared memory and limited local memory, alongside computational challenges, including specialized parallelism and sensitivity to imbalanced workloads. In this work, we propose CERESZII, an error-bounded lossy compressor that computes within Cerebras system. CereSZ-II addresses these challenges with a carefully optimized four-stage compression workflow, consisting of Pre-quantization, Lightweight Prediction, Fixed-size Huffman Encoding, and Spatial-aware Offset Computation, ensuring both memory efficiency and computational balance. Evaluation of several real-world scientific datasets shows that CERESZ-II achieves over 800 GB/s throughput, delivering high compression ratios and reliable reconstructed data quality.