Handling communication overhead in large-scale tensor-parallel training remains a critical challenge due to the dense, near-zero distributions of intermediate tensors, which exacerbate errors under frequent communication and introduce significant computational overhead during compression. To this end, we propose TACO (Tensor-parallel Adaptive COmmunication compression), a robust FP8-based framework for compressing TP intermediate tensors. First, we employ a data-driven reshaping strategy combined with an Adaptive Scale–Hadamard Transform to enable high-fidelity FP8 quantization, while its Dual-Scale Quantization mechanism ensures numerical stability throughout training. Second, we design a highly fused compression operator to reduce memory traffic and kernel launch overhead, allowing efficient overlap with communication. Finally, we integrate TACO with existing state-of-the-art methods for Data and Pipeline Parallelism to develop a compression-enabled 3D-parallel training framework. Detailed experiments on GPT models and Qwen model demonstrate up to 1.87 × end-to-end throughput improvement while maintaining near-lossless accuracy, validating the effectiveness and efficiency of TACO in large-scale training.

With the exponential growth of computing power, large-scale scientific simulations are producing massive volumes of data, leading to critical storage and I/O challenges. Error-bounded lossy compression has become one of the most effective solutions for reducing data size while preserving accuracy. Meanwhile, to achieve high-performance compression on such large datasets, leveraging GPUs has become increasingly essential. GPU-based lossy compressors deliver strong performance, but typically support only single-precision decompression, limiting their ability to meet the diverse accuracy requirements of scientific workflows. Progressive compressors can address this limitation by enabling on-demand precision retrieval. However, existing progressive lossy compressors on GPU still suffer from low throughput. To overcome these challenges, we present PRISM, a GPU-based progressive lossy compressor that achieves both high throughput and multi-precision retrieval, which introduces a high performance progressive framework that integrates the multiple interpolation predictors, efficient bitplane extraction, and an enhanced lossless compression that combines sign-absolute coding with zero-aware parallel algorithms. Evaluations on representative real-world datasets from five scientific domains show that PRISM significantly outperforms state-of-the-art progressive compressors on GPU, reducing retrieval data volume by over 15.6× and achieving up to 20.1× higher throughput on the NVIDIA H100 GPU under the same error bounds.

Burst buffers (BBs) act as an intermediate storage layer between compute nodes and parallel file systems (PFS), effectively alleviating the I/O performance gap in high-performance computing (HPC). As scientific simulations and AI workloads generate larger checkpoints and analysis outputs, BB capacity shortages and PFS bandwidth bottlenecks are emerging, and CPU-based compression is not an effective solution due to its high overhead. We introduceComputational Burst Buffers(CBBs), a storage paradigm that embeds hardware compression engines such as application-specific integrated circuit (ASIC) inside computational storage drives (CSDs) at the BB tier. CBB transparently offloads both lossless and error-bounded lossy compression from CPUs to CSDs, thereby (i) expanding effective SSD-backed BB capacity, (ii) reducing BB–PFS traffic, and (iii) eliminating contention and energy overheads of CPU-based compression. Unlike prior CSD-based compression designs targeting databases or flash caching, CBB co-designs the burst-buffer layer and CSD hardware for HPC and quantitatively evaluates compression offload in BB–PFS hierarchies. We prototype CBB using a PCIe 5.0 CSD with an ASIC Zstd-like compressor and an FPGA prototype of an SZ entropy encoder, and evaluate CBB on a 16-node cluster. Experiments with four representative HPC applications and a large-scale workflow simulator show up to 61% lower application runtime, 8–12× higher cache hit ratios, and substantially reduced compute-node CPU utilization compared to software compression and conventional BBs. These results demonstrate that compression-aware BBs with CSDs provide a practical, scalable path to next-generation HPC storage.

Today’s scientific simulations are confronting seriously limited I/O bandwidth, network bandwidth, and storage capacity because of immense volumes of data generated in high-performance computing systems. Data compression has emerged as one of the most effective approaches to resolve the issue of the exponential increase of scientific data. However, existing state-of-the-art compressors also are confronting the issue of low throughput, especially under the trend of growing disparities between the compute and I/O rates. Among them, embedded coding is widely applied, which contributes to the dominant running time for the corresponding compressors. In this work, we propose a new kind of embedded coding algorithm, and apply it as the backend embedded coding of ZFP, one of the most successful lossy compressors. Our embedded coding algorithm uses bit groups instead of bit planes to store the compressed data, avoiding the time overhead of generating bit planes and group tests of bit planes, which significantly reduces the running time of ZFP. Our embedded coding algorithm can also accelerate the decompression of ZFP, because the costly procedures of the reverse of group tests and reconstructing bit planes are also avoided. Moreover, we provide theoretical proof that the proposed coding algorithm has the same compression ratio as the baseline ZFP. Experiments with four representative real-world scientific simulation datasets show that the compression and decompression throughput of our solution is up to 2.5× (2.1× on average), and up to 2.1× (1.5× on average) as those of ZFP, respectively.