Tiling Framework for Heterogeneous Computing of Matrix-Based Tiled Algorithms

2023 2ND INTERNATIONAL WORKSHOP ON EXTREME HETEROGENEITY SOLUTIONS, EXHET 2023(2023)

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Abstract
Tiling matrix operations can improve the load balancing and performance of applications on heterogeneous computing resources. Writing a tile-based algorithm for each operation with a traditional, hand-tuned tiling approach that uses for loops in C/C++ is cumbersome and error prone. Moreover, it must enable and support the heterogeneous memory management of data objects and also explore architecture-supported, native, tiled-data transfer APIs instead of copying the tiled data to continuous memory before the data transfer. The tiling framework provides a tiled data structure for heterogeneous memory mapping and parameterization to a heterogeneous task specification API. We have integrated our tiled framework into MatRIS (Math kernels library using IRIS). IRIS is a heterogeneous run-time framework with a heterogeneous programming model, memory model, and task execution model. Experiments reveal that the tiled framework for BLAS operations has improved the programmability of tiled BLAS and improved performance by similar to 20% when compared against the traditional method that copies the data to continuous memory locations for heterogeneous computing.
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Key words
Heterogeneous computing, Tiling, IRIS, Heterogeneous memories, Task programming
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