high performance computing on graphics processing units: hgpu.org

Programming

hgpu.org » Programming

Generating Parallel OpenCL and OpenMP Programs from Dataflow Graphs

Evelyn Borth

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Tags: Benchmarking, Code generation, Computer science, OpenCL, OpenMP, Thesis

August 20, 2023 by hgpu

Porting Batched Iterative Solvers onto Intel GPUs with SYCL

Phuong Nguyen, Pratik Nayak, Hartwig Anzt

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Tags: Computer science, CUDA, Intel, Intel Data Center GPU Max 1550, nVidia, nVidia A100, nVidia H100, Package, performance portability, Physics, SYCL

August 20, 2023 by hgpu

Static and Dynamic Analyses for Efficient GPU Execution

Philip Munksgaard

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Tags: AMD Radeon Instinct MI100, ATI, Benchmarking, Computer science, nVidia, nVidia A100, OpenCL, Performance, Thesis

August 13, 2023 by hgpu

Efficiency without Tears: Securing Multilingual Programs with TRINITY

Hao Zhu, Baojian Hua, Xinrong Lin, Yufei Wu

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Tags: Computer science, Databases, Heterogeneous systems, HPC, Machine learning, OpenCL, Security

July 30, 2023 by hgpu

Monadic Deep Learning

Bo Yang, Zhihao Zhang Kirisame Marisa, Kai Shi

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Tags: Computer science, CUDA, Deep learning, Java, Neural networks, OpenCL, Package, Scala

July 30, 2023 by hgpu

Bandicoot: C++ Library for GPU Linear Algebra and Scientific Computing

Ryan R. Curtin, Marcus Edel, Conrad Sanderson

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Tags: Computer science, CUDA, Linear Algebra, nVidia, nVidia GeForce RTX 2080 Ti, OpenCL, Package, Programming techniques

July 30, 2023 by hgpu

Fast Knowledge Graph Completion using Graphics Processing Units

Chun-Hee Lee, Dong-oh Kang, Hwa Jeon Song

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Tags: AI, Algorithms, Computer science, CUDA, Databases, Graph theory, nVidia, nVidia A100

July 30, 2023 by hgpu

A portable C++ library for memory and compute abstraction on multi-core CPUs and GPUs

Pietro Incardona, Aryaman Gupta, Serhii Yaskovets, Ivo F. Sbalzarini

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Tags: AMD RX Vega 64, ATI, Computer science, CUDA, nVidia, nVidia A100, nVidia GeForce RTX 3090, OpenACC, OpenCL, OpenMP, Package, Performance, performance portability, SYCL

July 30, 2023 by hgpu

Creating a Dataset Supporting Translation Between OpenMP Fortran and C++ Code

Bin Lei, Caiwen Ding, Le Chen, Pei-Hung Lin, Chunhua Liao

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Tags: Computer science, CUDA, Fortran, Machine learning, nVidia, nVidia RTX 6000 Ada, OpenMP, Package

July 24, 2023 by hgpu

Maximizing Parallelism and GPU Utilization For Direct GPU Compilation Through Ensemble Execution

Shilei Tian, Barbara Chapman, Johannes Doerfert

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Tags: Benchmarking, Computer science, CUDA, LLVM, nVidia, nVidia A100, OpenMP, Performance

July 24, 2023 by hgpu

Towards Intelligent Runtime Framework for Distributed Heterogeneous Systems

Polykarpos Thomadakis

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Tags: Computer science, CUDA, Heterogeneous systems, Machine learning, Nuclear physics, nVidia, OpenCL, Physics, Task scheduling, Tesla V100, Thesis

July 16, 2023 by hgpu

Mystique: Enabling Accurate and Scalable Generation of Production AI Benchmarks

Mingyu Liang, Wenyin Fu, Louis Feng, Zhongyi Lin, Pavani Panakanti, Shengbao Zheng, Srinivas Sridharan, Christina Delimitrou

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Tags: AI, Benchmarking, Code generation, Computer science, CUDA, nVidia, Package, Performance, PyTorch, Tesla A100, Tesla V100

July 16, 2023 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

See all packages

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high performance computing on graphics processing units: hgpu.org

Programming

Generating Parallel OpenCL and OpenMP Programs from Dataflow Graphs

Porting Batched Iterative Solvers onto Intel GPUs with SYCL

Static and Dynamic Analyses for Efficient GPU Execution

Efficiency without Tears: Securing Multilingual Programs with TRINITY

Monadic Deep Learning

Bandicoot: C++ Library for GPU Linear Algebra and Scientific Computing

Fast Knowledge Graph Completion using Graphics Processing Units

A portable C++ library for memory and compute abstraction on multi-core CPUs and GPUs

Creating a Dataset Supporting Translation Between OpenMP Fortran and C++ Code

Maximizing Parallelism and GPU Utilization For Direct GPU Compilation Through Ensemble Execution

Towards Intelligent Runtime Framework for Distributed Heterogeneous Systems

Mystique: Enabling Accurate and Scalable Generation of Production AI Benchmarks

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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