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SYCLops: A SYCL Specific LLVM to MLIR Converter

Alexandre Singer, Frank (Fang) Gao, Kai-Ting Amy Wang

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Tags: Compilers, Computer science, Heterogeneous systems, LLVM, SYCL

May 15, 2022 by hgpu

Can We Run in Parallel? Automating Loop Parallelization for TornadoVM

Rishi Sharma, Shreyansh Kulshreshtha, Manas Thakur

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Tags: Computer science, FPGA, Heterogeneous systems, Java, OpenCL

May 15, 2022 by hgpu

FPGA Acceleration of Structured-Mesh-Based Explicit and Implicit Numerical Solvers using SYCL

K. Kamalakkannan, G.R. Mudalige, I.Z. Reguly, S.A. Fahmy

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Tags: Computer science, Design space exploration, FPGA, nVidia, SYCL, Tesla V100

May 8, 2022 by hgpu

Experience of Migrating a Parallel Graph Coloring Program from CUDA to SYCL

Zheming Jin

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Tags: Benchmarking, Computer science, CUDA, Graph theory, nVidia, OpenCL, Package, Performance, SYCL

May 8, 2022 by hgpu

cuPSO: GPU Parallelization for Particle Swarm Optimization Algorithms

Chuan-Chi Wang, Chun-Yen Ho, Chia-Heng Tu, Shih-Hao Hung

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 1080 Ti, Package, Particle swarm optimization

May 8, 2022 by hgpu

GPUNet: Searching the Deployable Convolution Neural Networks for GPUs

Linnan Wang, Chenhan Yu, Satish Salian, Slawomir Kierat, Szymon Migacz, Alex Fit Florea

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Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia Quadro GV100, Performance, Tesla A100

May 8, 2022 by hgpu

Analytical Performance Estimation during Code Generation on Modern GPUs

Dominik Ernst, Markus Holzer, Georg Hager, Matthias Knorr, Gerhard Wellein

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Tags: Code generation, Computer science, CUDA, Lattice Boltzmann model, Machine learning, nVidia, OpenCL, Package, Tesla A100, Tesla V100

May 8, 2022 by hgpu

The Celerity High-level API: C++20 for Accelerator Clusters

Peter Thoman, Florian Tischler, Philip Salzmann, Thomas Fahringer

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Tags: Code generation, Computer science, Data parallelism, MPI, nVidia, nVidia GeForce RTX 2070, Package, Programming techniques, SYCL

May 1, 2022 by hgpu

CASE: A Compiler-Assisted SchEduling Framework for Multi-GPU Systems

Chao Chen, Chris Porter, Santosh Pande

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Tags: Compilers, Computer science, CUDA, nVidia, Package, Task scheduling, Tesla P100, Tesla V100

May 1, 2022 by hgpu

End-to-end Mapping in Heterogeneous Systems Using Graph Representation Learning

Yao Xiao, Guixiang Ma, Nesreen K. Ahmed, Mihai Capota, Theodore Willke, Shahin Nazarian, Paul Bogdan

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Tags: Computer science, Heterogeneous systems, Machine learning, Neural networks, OpenCL

May 1, 2022 by hgpu

Improving performance of SYCL applications on CPU architectures using LLVM-directed compilation flow

Pietro Ghiglio, Uwe Dolinsky, Mehdi Goli, Kumudha Narasimhan

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Tags: Computer science, CUDA, nVidia, OpenCL, Performance, performance portability, SYCL

May 1, 2022 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

Applications

SYCLops: A SYCL Specific LLVM to MLIR Converter

Can We Run in Parallel? Automating Loop Parallelization for TornadoVM

FPGA Acceleration of Structured-Mesh-Based Explicit and Implicit Numerical Solvers using SYCL

Experience of Migrating a Parallel Graph Coloring Program from CUDA to SYCL

cuPSO: GPU Parallelization for Particle Swarm Optimization Algorithms

GPUNet: Searching the Deployable Convolution Neural Networks for GPUs

Analytical Performance Estimation during Code Generation on Modern GPUs

The Celerity High-level API: C++20 for Accelerator Clusters

CASE: A Compiler-Assisted SchEduling Framework for Multi-GPU Systems

End-to-end Mapping in Heterogeneous Systems Using Graph Representation Learning

Improving performance of SYCL applications on CPU architectures using LLVM-directed compilation flow

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)