<ul><li>During Large Language Models (LLMs) training, a significant amount of tensor data is checkpointed periodically for recovery purposes in case of failure.</li><li>The paper focuses on optimizing the checkpointing process by analyzing checkpoint data and maximizing the use of lossless compression techniques to reduce the data volume.</li><li>An effective compression solution named Language Model Compressor (LMC) has been developed, based on byte-grouping and Huffman encoding, offering better performance than existing alternatives like BZ2 with significantly reduced compression time.</li><li>LMC's 16-core parallel implementation achieves high compression and decompression throughput, leading to reduced CPU resources and enabling higher-frequency checkpoints during model training.</li></ul>

Lossless Compression for LLM Tensor Incremental Snapshots

Discover more