high performance computing on graphics processing units: hgpu.org

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PROGRAML: A Graph-based Program Representation for Data Flow Analysis and Compiler Optimizations

Chris Cummins, Zacharias V. Fisches, Tal Ben-Nun, Torsten Hoefler, Michael O'Boyle, Hugh Leather

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Tags: Compilers, Computer science, Flow analysis, Machine learning, OpenCL, Package, Performance

January 2, 2022 by hgpu

Optimization of Compiler-generated OpenCL CNN Kernels and Runtime for FPGAs

Seung-Hun Chung

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Tags: Computer science, FPGA, Neural networks, nVidia, nVidia GeForce GTX 1060, OpenCL, Performance, Thesis

December 19, 2021 by hgpu

Analysis and Comparison of Performance and Power Consumption of Neural Networks on CPU, GPU, TPU and FPGA

Christopher Noel Hesse

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Tags: Benchmarking, Computer science, Deep learning, FPGA, Neural networks, nVidia, nVidia DGX-1, nVidia GeForce RTX 2070, nVidia Jetson Nano, Performance, Tesla V100

December 5, 2021 by hgpu

Bayesian Optimization for auto-tuning GPU kernels

Floris-Jan Willemsen, Rob van Nieuwpoort, Ben van Werkhoven

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Tags: Bayesian, Computer science, CUDA, Machine learning, nVidia, nVidia A100, nVidia GeForce GTX Titan X, nVidia GeForce RTX 2070, OpenCL, Package, Performance

December 5, 2021 by hgpu

Generating GPU Compiler Heuristics using Reinforcement Learning

Ian Colbert, Jake Daly, Norm Rubin

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Tags: AMD Radeon 6800 XT, AMD Radeon RX 5700 XT, ATI, Compilers, Computer science, Machine learning, Performance

November 28, 2021 by hgpu

Predictive Data Race Detection for GPUs

Sagnik Dey, Mayant Mukul, Parth Sharma, Swarnendu Biswas

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia Quadro RTX 5000, Performance

November 28, 2021 by hgpu

BAT: A Benchmark suite for AutoTuners

Ingunn Sund, Knut A. Kirkhorn, Jacob O. Tørring, Anne C. Elster

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Tags: Auto-Tuning, Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 980, Package, Performance, Tesla T4, Tesla V100

November 21, 2021 by hgpu

Performance Optimisations for Heterogeneous Managed Runtime Systems

Michail Papadimitriou

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Tags: Computer science, FPGA, Heterogeneous systems, Java, nVidia, nVidia GeForce GTX 1650, OpenCL, Performance, Thesis

November 14, 2021 by hgpu

Performance Evaluation of Python ParallelProgramming Models: Charm4Py and mpi4py

Zane Fink, Simeng Liu, Jaemin Choi, Matthias Diener, Laxmikant V. Kale

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Tags: Benchmarking, Computer science, CUDA, Distributed computing, HPC, Machine learning, MPI, nVidia, Package, Performance, Python, Tesla V100

November 14, 2021 by hgpu

iGUARD: In-GPU Advanced Race Detection

Aditya K Kamath, Arkaprava Basu

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Tags: Computer science, CUDA, nVidia, nVidia Titan RTX, Package, Performance

November 7, 2021 by hgpu

Optimizing a Hardware Network Stack to Realize an In-Network ML Inference Application

Marco Hartmann, Lukas Weber, Johannes Wirth, Lukas Sommer, Andreas Koch

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Tags: Computer science, FPGA, Machine learning, Performance

November 7, 2021 by hgpu

Source-to-Source Automatic Differentiation of OpenMP Parallel Loops

Jan Hückelheim, Laurent Hascoët

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Tags: Code generation, Computer science, Machine learning, OpenMP, Package, Performance

November 7, 2021 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

PROGRAML: A Graph-based Program Representation for Data Flow Analysis and Compiler Optimizations

Optimization of Compiler-generated OpenCL CNN Kernels and Runtime for FPGAs

Analysis and Comparison of Performance and Power Consumption of Neural Networks on CPU, GPU, TPU and FPGA

Bayesian Optimization for auto-tuning GPU kernels

Generating GPU Compiler Heuristics using Reinforcement Learning

Predictive Data Race Detection for GPUs

BAT: A Benchmark suite for AutoTuners

Performance Optimisations for Heterogeneous Managed Runtime Systems

Performance Evaluation of Python ParallelProgramming Models: Charm4Py and mpi4py

iGUARD: In-GPU Advanced Race Detection

Optimizing a Hardware Network Stack to Realize an In-Network ML Inference Application

Source-to-Source Automatic Differentiation of OpenMP Parallel Loops

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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