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• W2984946966 abstract "Sparse solvers are heavily used in computational fluid dynamics (CFD), computer-aided design (CAD), and other important application domains. These solvers remain challenging to execute on massively parallel architectures, due to the sequential dependencies between the fine-grained application tasks. In particular, parallel sparse solvers typically suffer from substantial scheduling and dependency-management overheads relative to the compute operations. We propose adaptive task aggregation (ATA) to efficiently execute such irregular computations on GPU architectures via hierarchical dependency management and low-latency task scheduling. On a gamut of representative problems with different data-dependency structures, ATA significantly outperforms existing GPU task-execution approaches, achieving a geometric mean speedup of 2.2X to 3.7X across different sparse kernels (with speedups of up to two orders of magnitude)." @default.
• W2984946966 created "2019-11-22" @default.
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• W2984946966 date "2019-09-01" @default.
• W2984946966 modified "2023-10-06" @default.
• W2984946966 title "Adaptive Task Aggregation for High-Performance Sparse Solvers on GPUs" @default.
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• W2984946966 doi "https://doi.org/10.1109/pact.2019.00033" @default.
• W2984946966 hasPublicationYear "2019" @default.
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