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GPU-Acceleration of Tensor Renormalization with PyTorch using CUDA

Raghav G. Jha, Abhishek Samlodia

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Tags: CUDA, High Energy Physics - Lattice, nVidia, nVidia GeForce RTX 2080 Ti, Physics, PyTorch

June 4, 2023 by hgpu

Compiler Technologies in Deep Learning Co-Design: A Survey

Hongbin Zhang, Mingjie Xing, Yanjun Wu, Chen Zhao

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Tags: Compielrs, Computer science, Deep learning, HLS, OpenCL, Package, survey

June 4, 2023 by hgpu

A High-Performance Computing Cluster for Distributed Deep Learning: A Practical Case of Weed Classification Using Convolutional Neural Network Models

Manuel López-Martínez, Germán Díaz-Flórez, Santiago Villagrana-Barraza, Luis O. Solís-Sánchez, Héctor A. Guerrero-Osuna, Genaro M. Soto-Zarazúa, Carlos A. Olvera-Olvera

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Tags: CUDA, Deep learning, Distributed computing, HPC, Image processing, Neural networks, nVidia, nVidia GeForce GTX 1050 Ti

June 4, 2023 by hgpu

Implementation Techniques for SPMD Kernels on CPUs

Joachim Meyer, Aksel Alpay, Sebastian Hack, Holger Fröning, Vincent Heuveline

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Tags: Compilers, Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL, Package, performance portability

June 4, 2023 by hgpu

Genomics-GPU: A Benchmark Suite for GPU-accelerated Genome Analysis

Zhuren Liu, Shouzhe Zhang, Justin Garrigus, Hui Zhao

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Tags: Benchmarking, Bioinformatics, Biology, Computer science, CUDA, Genomics, nVidia, nVidia GeForce RTX 3070, Package

May 28, 2023 by hgpu

Experiences Migrating CUDA to SYCL: A Molecular Docking Case Study

Leonardo Solis-Vasquez, Edward Mascarenhas, Andreas Koch

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Tags: Chemistry, Computer science, CUDA, molecular docking, nVidia, nVidia A100, oneAPI, Package, SYCL

May 28, 2023 by hgpu

PyTorch Hyperparameter Tuning – A Tutorial for spotPython

Thomas Bartz-Beielstein

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Tags: Computer science, Deep learning, Package, Python, PyTorch, Tutorial

May 28, 2023 by hgpu

Communication-minimizing Asynchronous Tensor Parallelism

Siddharth Singh, Zack Sating, Abhinav Bhatele

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Tags: Computer science, CUDA, GPU cluster, Neural networks, nVidia, nVidia A100

May 28, 2023 by hgpu

ACRoBat: Optimizing Auto-batching of Dynamic Deep Learning at Compile Time

Pratik Fegade, Tianqi Chen, Phillip B. Gibbons, Todd C. Mowry

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Tags: Code generation, Computer science, CUDA, Deep learning, nVidia, nVidia GeForce RTX 3070

May 28, 2023 by hgpu

An Asynchronous Dataflow-Driven Execution Model For Distributed Accelerator Computing

Philip Salzmann, Fabian Knorr, Peter Thoman, Philipp Gschwandtner, Biagio Cosenza, Thomas Fahringer

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Tags: Benchmarking, Computer science, CUDA, GPU cluster, MPI, nVidia, nVidia V100, Package, SYCL

May 21, 2023 by hgpu

Dragon-Alpha&cu32: A Java-based Tensor Computing Framework With its High-Performance CUDA Library

Zhiyi Zhang, Pengfei Zhang, Qi Wang

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Tags: Computer science, CUDA, Deep learning, Heterogeneous systems, Java, nVidia, nVidia GeForce RTX 3060 Ti, Package

May 21, 2023 by hgpu

Improving Energy Efficiency of Basic Linear Algebra Routines on Heterogeneous Systems with Multiple GPUs

Hadi Zamani Sabzi

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Tags: Algorithms, Computer science, CUDA, Energy-efficient computing, Factorization, Heterogeneous systems, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce GTX 1660, nVidia GeForce GTX 980, Thesis

May 21, 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

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high performance computing on graphics processing units: hgpu.org

Applications

GPU-Acceleration of Tensor Renormalization with PyTorch using CUDA

Compiler Technologies in Deep Learning Co-Design: A Survey

A High-Performance Computing Cluster for Distributed Deep Learning: A Practical Case of Weed Classification Using Convolutional Neural Network Models

Implementation Techniques for SPMD Kernels on CPUs

Genomics-GPU: A Benchmark Suite for GPU-accelerated Genome Analysis

Experiences Migrating CUDA to SYCL: A Molecular Docking Case Study

PyTorch Hyperparameter Tuning – A Tutorial for spotPython

Communication-minimizing Asynchronous Tensor Parallelism

ACRoBat: Optimizing Auto-batching of Dynamic Deep Learning at Compile Time

An Asynchronous Dataflow-Driven Execution Model For Distributed Accelerator Computing

Dragon-Alpha&cu32: A Java-based Tensor Computing Framework With its High-Performance CUDA Library

Improving Energy Efficiency of Basic Linear Algebra Routines on Heterogeneous Systems with Multiple GPUs

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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