The amount of scientific data being produced, transferred, and processed increases rapidly. Whereas GPUs have made faster processing possible, storage limitations and slow data transfers remain key bottlenecks. Data compression can help, but only if it does not create a new bottleneck. This paper presents four new lossless compression algorithms for single- and double-precision data that compress well and are fast even though they are fully compatible between CPUs and GPUs. Averaged over many SDRBench inputs, our implementations outperform most of the 18 compressors from the literature we compare to in compression ratio, compression throughput, and decompression throughput. Moreover, they outperform all of them in either throughput or compression ratio on the two CPUs and two GPUs we used for evaluation. For example, on an RTX 4090 GPU, our fastest code compresses and decompresses at over 500 GB/s while delivering one of the highest compression ratios.

Scientific simulations can produce petabytes of data in a single run. Even when aggressively compressed, such data cannot be stored locally on a scientist's workstation. This paper introduces omni-homomorphic compression (oHC), a technique to generate extremely compressed versions of the data that are still useful and that can be processed locally without the need for decompression. Since the main data is kept separate, it can be preserved at any user-specified error bound and retrieved at will. Compared to other lossy and/or homomorphic compressors, oHC delivers not only orders of magnitude higher compression ratios but also some of the highest throughputs. For example, running on an RTX 4090 GPU with an error bound of 1E-2 on single-precision data, it provides an average of 270 and$253 ~\text{GB} / \mathrm{s}$throughput for compression and decompression, respectively. When extracting the arithmetic mean from a large dataset, oHC's extremely compressed version is 95,000 times faster than the fastest homomorphic compressor from the literature.

High-throughput data compression is increasingly important for large scientific projects. This paper presents PFPL, a lossy floating-point data compressor with guaranteed error bounds that is fully compatible between CPUs and GPUs. Despite this compatibility, PFPL delivers some of the highest compression and decompression speeds and compression ratios on both single- and double-precision data. For example, using an absolute error bound of$1 \mathrm{E}-3$, it yields a single-precision compression throughput on the SDRBench inputs of$5 \text{GB} / \mathrm{s}$on a Ryzen 2950X CPU and 423 GB/s on an RTX 4090 GPU. This is at least 4.6 times higher than the throughput of seven leading compressors on both devices. Moreover, PFPL's compression ratio is higher than that of all tested GPU codes.

Finding a minimum spanning tree (MST) is a fundamental graph algorithm with applications in many fields. This paper presents ECL-MST, a fast MST implementation designed specifically for GPUs. ECL-MST is based on a parallelization approach that unifies Kruskal's and Borůvka's algorithm and incorporates new and existing optimizations from the literature, including implicit path compression and edge-centric operation. On two test systems, it outperforms leading GPU and CPU codes from the literature on all of our 17 input graphs from various domains. On a Titan V GPU, ECL-MST is, on average, 4.6 times faster than the next fastest code, and on an RTX 3080 Ti GPU, it is 4.5 times faster. On both systems, ECL-MST running on the GPU is roughly 30 times faster than the fastest parallel CPU code.