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Optimizing Block-Sparse Matrix Multiplications on CUDA with TVM

Zijing Gu

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Tags: Computer science, CUDA, Deep learning, Machine learning, Matrix multiplication, nVidia, Package, Sparse matrix, Tesla T4

August 2, 2020 by hgpu

GE-SpMM: General-purpose Sparse Matrix-Matrix Multiplication on GPUs for Graph Neural Networks

Guyue Huang, Guohao Dai, Yu Wang, Huazhong Yang

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Tags: Computer science, CUDA, Linear Algebra, Matrix multiplication, Neural networks, nVidia, nVidia GeForce GTX 1080 Ti, nVidia GeForce RTX 2080, Package, Sparse matrix

July 12, 2020 by hgpu

Adaptive SpMV/SpMSpV on GPUs for Input Vectors of Varied Sparsity

Min Li, Yulong Ao, Chao Yang

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Tags: Computer science, CUDA, Machine learning, nVidia, Sparse matrix, Tesla K40, Tesla P100, Tesla V100

July 5, 2020 by hgpu

Ginkgo: A Modern Linear Operator Algebra Framework for High Performance Computing

Hartwig Anzt, Terry Cojean, Goran Flegar, Fritz Göbel, Thomas Grützmacher, Pratik Nayak, Tobias Ribizel, Yuhsiang Mike Tsai, Enrique S. Quintana-Ortí

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Tags: Algorithms, AMD Radeon VII, ATI, Computer science, CUDA, HIP, Linear Algebra, Mathematical Software, nVidia, Package, Sparse, Sparse matrix, Tesla V100

July 5, 2020 by hgpu

GPU-based matrix-free finite element solver exploiting symmetry of elemental matrices

Utpal Kiran, Sachin Singh Gautam, Deepak Sharma

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Tags: Computer science, CUDA, FEM, Finite element method, nVidia, Sparse matrix, Tesla K40

June 28, 2020 by hgpu

Sparse GPU Kernels for Deep Learning

Trevor Gale, Matei Zaharia, Cliff Young, Erich Elsen

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Tags: Computer science, CUDA, Deep learning, Machine learning, Matrix multiplication, Neural networks, nVidia, Sparse matrix, Tesla V100

June 28, 2020 by hgpu

Efficient Sparse-Dense Matrix-Matrix Multiplication on GPUs Using the Customized Sparse Storage Format

Shaohuai Shi, Qiang Wang, Xiaowen Chu

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Tags: Algorithms, Computer science, CUBLAS, CUDA, Matrix multiplication, nVidia, nVidia GeForce GTX 980, nVidia GeForce GTX Titan X, Package, Sparse matrix, Tesla P100

June 7, 2020 by hgpu

PDAWL: Profile-based Iterative Dynamic Adaptive WorkLoad Balance on Heterogeneous Architectures

Tongsheng Geng, Marcos Amaris, Stephane Zuckerman, Alfredo Goldman, Guang R. Gao, Jean-Luc Gaudiot

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Tags: Computer science, CUDA, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce GTX Titan, Performance, Sparse matrix, Task scheduling, Tesla K20, Tesla K40

May 24, 2020 by hgpu

Using Machine Learning to Estimate Utilization and Throughput for OpenCL-Based SpMV Implementation on an FPGA

Jannatun Naher, Clay Gloster, Shrikant S. Jadhav, Christopher C. Doss

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Tags: Computer science, Design space exploration, DSP, FPGA, Linear Algebra, Machine learning, OpenCL, Sparse matrix

April 12, 2020 by hgpu

A Systematic Survey of General Sparse Matrix-Matrix Multiplication

Jianhua Gao, Weixing Ji, Zhaonian Tan, Yueyan Zhao

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Tags: Algorithms, Computer science, CUDA, Matrix multiplication, nVidia, nVidia GeForce GTX 1080 Ti, Sparse matrix, survey

March 1, 2020 by hgpu

A Unified Iteration Space Transformation Framework for Sparse and Dense Tensor Algebra

Ryan Senanayake, Fredrik Kjolstad, Changwan Hong, Shoaib Kamil, Saman Amarasinghe

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Tags: Code generation, Compilers, Computer science, CUDA, Linear Algebra, nVidia, nVidia DGX-1, Package, Sparse matrix, Tesla V100

January 5, 2020 by hgpu

Sparse matrix partitioning for optimizing SpMV on CPU-GPU heterogeneous platforms

Akrem Benatia, Weixing Ji, Yizhuo Wang, Feng Shi

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX Titan X, Sparse matrix

January 5, 2020 by hgpu

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

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high performance computing on graphics processing units: hgpu.org

Optimizing Block-Sparse Matrix Multiplications on CUDA with TVM

GE-SpMM: General-purpose Sparse Matrix-Matrix Multiplication on GPUs for Graph Neural Networks

Adaptive SpMV/SpMSpV on GPUs for Input Vectors of Varied Sparsity

Ginkgo: A Modern Linear Operator Algebra Framework for High Performance Computing

GPU-based matrix-free finite element solver exploiting symmetry of elemental matrices

Sparse GPU Kernels for Deep Learning

Efficient Sparse-Dense Matrix-Matrix Multiplication on GPUs Using the Customized Sparse Storage Format

PDAWL: Profile-based Iterative Dynamic Adaptive WorkLoad Balance on Heterogeneous Architectures

Using Machine Learning to Estimate Utilization and Throughput for OpenCL-Based SpMV Implementation on an FPGA

A Systematic Survey of General Sparse Matrix-Matrix Multiplication

A Unified Iteration Space Transformation Framework for Sparse and Dense Tensor Algebra

Sparse matrix partitioning for optimizing SpMV on CPU-GPU heterogeneous platforms

Recent source codes

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

OpenMC Monte Carlo Code

Polygeist: C/C++ frontend for MLIR

Parallel Gaussian process with kernel approximation in CUDA

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