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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

Pipelined Training with Stale Weights of Deep Convolutional Neural Networks

Lifu Zhang, Tarek S. Abdelrahman

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Tags: Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 1060, PyTorch

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

LLVM-based automation of memory decoupling for OpenCL applications on FPGAs

Arnab A Purkayastha, Samuel Rogers, Suhas A Shiddhibhabi, Hamed Tabkhi

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Tags: Computer science, FPGA, LLVM, OpenCL, Package

January 5, 2020 by hgpu

Towards Unified INT8 Training for Convolutional Neural Network

Feng Zhu, Ruihao Gong, Fengwei Yu, Xianglong Liu, Yanfei Wang, Zhelong Li, Xiuqi Yang, Junjie Yan

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Tags: Computer science, Deep learning, Neural networks, nVidia, nVidia GeForce GTX 1080 Ti

January 5, 2020 by hgpu

Abstractions for Programming Graphics Processors in High-Level Programming Languages

Tim Besard

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Tags: Computer science, CUDA, Heterogeneous systems, High-level Languages, LLVM, nVidia, nVidia GeForce GTX 1080, Package, Thesis

December 29, 2019 by hgpu

Automatic Performance Optimisation of Parallel Programs for GPUs via Rewrite Rules

Toomas Remmelg

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Tags: AMD Radeon R9 295X2, ARM, ATI, Computer science, nVidia, nVidia GeForce GTX Titan Black, OpenCL, Performance, performance portability, Thesis

December 29, 2019 by hgpu

Porting tree-based hash table compression to GPGPU model checking

Danny Bergsma

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Tags: Algorithms, Computer science, CUDA, Hashing, nVidia, nVidia GeForce GTX Titan XP, Package, Thesis

December 29, 2019 by hgpu

Accelerating Molecular Docking by Parallelized Heterogeneous Computing – A Case Study of Performance, Quality of Results, and Energy-Efficiency using CPUs, GPUs, and FPGAs

Leonardo Solis Vasquez

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Tags: AMD Radeon RX Vega 56, ATI, Computer science, FPGA, Heterogeneous systems, Macromolecule, nVidia, OpenCL, Package, Physics, Tesla V100, Thesis

December 29, 2019 by hgpu

libmolgrid: GPU Accelerated Molecular Gridding for Deep Learning Applications

Jocelyn Sunseri, David Ryan Koes

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Tags: Chemistry, Computer science, CUDA, Deep learning, Machine learning, Molecular modeling, Neural networks, nVidia, nVidia GeForce GTX Titan V, nVidia GeForce GTX Titan X, Package, PyTorch, TensorFlow

December 15, 2019 by hgpu

Array Languages Make Neural Networks Fast

Artjoms Šinkarovs, Hans-Nikolai Vießmann, Sven-Bodo Scholz

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Tags: Computer science, CUDA, High-level Languages, nVidia, Package, Python, PyTorch, TensorFlow, Tesla K20

December 15, 2019 by hgpu

The BondMachine toolkit: Enabling Machine Learning on FPGA

Mirko Mariotti, Loriano Storchi, Daniele Spiga, Davide Salomoni, Tommaso Boccali, Daniele Bonacorsi

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Tags: Computer science, FPGA, Go, Machine learning

December 8, 2019 by hgpu

CuPBoP-AMD: Extending CUDA to AMD Platforms

CuPBoP: Making CUDA a Portable Language

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

Automated Deep Learning Optimization via DSL-Based Source Code Transformation

ROCm's implementation of Gromacs

GROMACS on AMD GPU-Based HPC Platforms: Using SYCL for Performance and Portability

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

Experiences with implementing Kokkos’ SYCL backend

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

See all packages

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high performance computing on graphics processing units: hgpu.org

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

Pipelined Training with Stale Weights of Deep Convolutional Neural Networks

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

LLVM-based automation of memory decoupling for OpenCL applications on FPGAs

Towards Unified INT8 Training for Convolutional Neural Network

Abstractions for Programming Graphics Processors in High-Level Programming Languages

Automatic Performance Optimisation of Parallel Programs for GPUs via Rewrite Rules

Porting tree-based hash table compression to GPGPU model checking

Accelerating Molecular Docking by Parallelized Heterogeneous Computing – A Case Study of Performance, Quality of Results, and Energy-Efficiency using CPUs, GPUs, and FPGAs

libmolgrid: GPU Accelerated Molecular Gridding for Deep Learning Applications

Array Languages Make Neural Networks Fast

The BondMachine toolkit: Enabling Machine Learning on FPGA

Recent source codes

CuPBoP-AMD: Extending CUDA to AMD Platforms

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

ROCm's implementation of Gromacs

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

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

Most viewed papers (last 30 days)