Existing GPU lossy compressors suffer from expensive data movement overheads,
inefficient memory access patterns, and high synchronization latency, resulting in limited …

Error-bounded lossy compression is a critical technique for significantly reducing scientific
data volumes. Compared to CPU-based compressors, GPU-based compressors exhibit …

GPU-aware collective communication has become a major bottleneck for modern computing
platforms as GPU computing power rapidly rises. A traditional approach is to directly …

Today's scientific applications running on supercomputers produce large volumes of data,
leading to critical data storage and communication challenges. To tackle the challenges …

Error-bounded lossy compression has been a critical technique to significantly reduce the
sheer amounts of simulation datasets for high-performance computing (HPC) scientific …

Full-state quantum circuit simulation requires exponentially increased memory size to store
the state vector as the number of qubits scales, presenting significant limitations in classical …

As network bandwidth struggles to keep up with rapidly growing computing capabilities, the
efficiency of collective communication has become a critical challenge for exa-scale …

GPU-aware collective communication has become a major bottleneck for modern computing
platforms as GPU computing power rapidly rises. To address this issue, traditional …

Error-bounded lossy compression has been effective in significantly reducing the data
storage/transfer burden while preserving the reconstructed data fidelity very well. Many error …

Lossy compression can be an effective tool in AI training and inference to reduce memory
requirements, storage footprint, and in some cases, execution time. With the rise of novel …