Sparse-Matrix Compression Primitives with OpenCL Framework to Support Halide

@article{Lee2019SparseMatrixCP,
  title={Sparse-Matrix Compression Primitives with OpenCL Framework to Support Halide},
  author={Chao-Lin Lee and Chen-Ting Chao and Jenq-Kuen Lee and Chung-Wen Huang and Ming-Yu Hung},
  journal={Proceedings of the International Workshop on OpenCL},
  year={2019}
}
Halide and OpenCL now play important roles for heterogeneous multi-core computing. OpenCL provides vendor-level support and Halide provides domain-specific support such as vision processing and AI model (TVM Halide IR). Halide also provides flexible scheduling for applications on target machines. OpenCL plays a supporting role for Halide environments. In this work, we investigate the research issues in supporting sparse computation with Halide and their corresponding OpenCL support. We present… 
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References

SHOWING 1-5 OF 5 REFERENCES

Efficient sparse-matrix multi-vector product on GPUs

An in-depth analysis is presented to contrast SpMV and SpMM, and a new sparse-matrix representation and computation approach suited to achieving high data-movement efficiency and effective GPU parallelization of SpMM is developed.

Support and optimization for parallel sparse programs with array intrinsics of Fortran 90

Decoupling algorithms from schedules for easy optimization of image processing pipelines

This work proposes a representation for feed-forward imaging pipelines that separates the algorithm from its schedule, enabling high-performance without sacrificing code clarity, and demonstrates the power of this representation by expressing a range of recent image processing applications in an embedded domain specific language called Halide and compiling them for ARM, x86, and GPUs.

Halide: a language and compiler for optimizing parallelism, locality, and recomputation in image processing pipelines

A systematic model of the tradeoff space fundamental to stencil pipelines is presented, a schedule representation which describes concrete points in this space for each stage in an image processing pipeline, and an optimizing compiler for the Halide image processing language that synthesizes high performance implementations from a Halide algorithm and a schedule are presented.

Halide

A new programming language for image processing pipelines, called Halide, that separates the algorithm from its schedule, and is expressive enough to describe organizations that match or outperform state-of-the-art hand-written implementations of many computational photography and computer vision algorithms.