HPC applications generate massive amounts of data that impose significant burdens on both storage and I/O systems. Although lossy compressors have been widely adopted in this scenario to reduce data volume, the SOTA approach fails to fully exploit redundancy because it relies on separate techniques that operate at incompatible granularities. Their suboptimal compression ratios leave I/O as the dominant bottleneck in data dumps/loads. Therefore, we propose MAC, a compression framework built upon existing SZ compressor. It leverages the alignment between HPC system characteristics and modeling-based arithmetic coding to balance the compression ratio improvement and time cost. Instead of applying Huffman coding and dictionary-based compressors like zstd or gzip sequentially on quantization factors as SZ does, MAC replaces them with an adaptive arithmetic encoder. Specifically, MAC first employs bit-packing on incoming quantization factors to reduce overhead, as these factors are typically small enough that standard 4-byte storage would impede processing efficiency. The system then constructs context with the knowledge of the length of each quantization factor, utilizing hash tables to store and retrieve historical occurrences. By leveraging two models with distinct prefix-matching strategies and integrating them via a logistic mixer, MAC yields substantial compression gains. This architecture ensures that compression and decompression latencies remain low enough to accelerate overall dump and load operations. Experiments show that MACSZ achieves a compression ratio improvement of over 25%, which translates directly into enhanced throughput on HPC cluster architectures as Fig 1 and 2 demonstrate.

The explosive growth of global data has created an increasing demand for archival storage, where efficient compression is crucial to reduce capacity cost. However, existing archival compressors face a fundamental tradeoff: mainstream methods (e.g., ZSTD with level$21 / 22$) offer limited compression ratios, while context-mixing compressors (e.g., LPAQ) achieve higher ratios but are often too slow for practical use. Therefore, we present CuCM, a GPU-powered context-mixing compressor to overcome this tradeoff. By introducing pre-learning and batch update mechanisms, CuCM resolves the data dependencies inherent in the autoregressive modeling of contextmixing compressors. During compression, CuCM processes each predefined vector as a single unit. It utilizes the current model to predict the probability distribution for the entire vector, deferring model updates until the vector is fully processed. During decompression, CuCM employs an aggressive look-ahead strategy, preassuming bit values for context construction. It then retains only the outcomes of hypotheses that remain consistent with the actual decoded data. Experiments like figure 1 show that CuCM achieves up to$12.6 \times$higher throughput than LPAQ while maintaining comparable compression ratios across both general-purpose and archival datasets.