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Subgraph pattern matching is fundamental to graph analytics and has wide applications. Unfortunately, high computational complexity limits the robustness guarantees of existing algorithms: they do not scale for modern large graph datasets and/or they have limitations in terms of accuracy or in terms of the intricacy of the patterns supported. We present algorithms, theory, and empirical evidence that...
Multi-/many-core CPU based architectures are seeing widespread adoption due to their unprecedented compute performance in a small power envelope. With the increasingly large number of cores on each node, applications spend a significant portion of their execution time in intra-node communication. While shared memory is commonly used for intra-node communication, it needs to copy each message once...
We investigate efficient sensitivity analysis (SA) of algorithms that segment and classify image features in a large dataset of high-resolution images. Algorithm SA is the process of evaluating variations of methods and parameter values to quantify differences in the output. A SA can be very compute demanding because it requires re-processing the input dataset several times with different parameters...
The availability and amount of sequenced genomes have been rapidly growing in recent years because of the adoption of next-generation sequencing (NGS) technologies that enable high-throughput short-read generation at highly competitive cost. Since this trend is expected to continue in the foreseeable future, the design and implementation of efficient and scalable NGS bioinformatics algorithms are...
Sparse tensors appear in many large-scale applications with multidimensional and sparse data. While multidimensional sparse data often need to be processed on manycore processors, attempts to develop highly-optimized GPU-based implementations of sparse tensor operations are rare. The irregular computation patterns and sparsity structures as well as the large memory footprints of sparse tensor operations...
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