high performance computing on graphics processing units: hgpu.org

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hgpu.org » Applications

Kernel-as-a-Service: A Serverless Interface to GPUs

Nathan Pemberton, Anton Zabreyko, Zhoujie Ding, Randy Katz, Joseph Gonzalez

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Tags: Cloud, Computer science, CUDA, Deep learning, Distributed computing, nVidia, Package, Tesla V100

December 25, 2022 by hgpu

Portable C++ Code that can Look and Feel Like Fortran Code with Yet Another Kernel Launcher (YAKL)

Matthew Norman, Isaac Lyngaas, Abhishek Bagusetty, Mark Berrill

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Tags: Computer science, Fortran, nVidia, Package, SYCL, Tesla V100

December 19, 2022 by hgpu

Assessing Application Efficiency and Performance Portability in Single-Source Programming for Heterogeneous Parallel Systems

August Ernstsson, Dalvan Griebler, Christoph Kessler

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL, OpenMP, Package, performance portability, Tesla K20

December 11, 2022 by hgpu

Precise Energy Consumption Measurements of Heterogeneous Artificial Intelligence Workloads

René Caspart, Sebastian Ziegler, Arvid Weyrauch, Holger Obermaier, Simon Raffeiner, Leon Pascal Schuhmacher, Jan Scholtyssek, Darya Trofimova, Marco Nolden, Ines Reinartz, Fabian Isensee, Markus Götz, Charlotte Debus

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Tags: Artificial intelligence, Computer science, Deep learning, Energy-efficient computing, Heterogeneous systems, nVidia, nVidia A100, nVidia V100, Package

December 11, 2022 by hgpu

Three Contributions to the Theory and Practice of Optimizing Compilers

Linxiao Wang

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Tags: Compilers, Computer science, CUDA, LLVM, nVidia, nVidia GeForce GTX 1080 Ti, nVidia GeForce GTX 760 M, Package, Thesis

December 4, 2022 by hgpu

Galvatron: Efficient Transformer Training over Multiple GPUs Using Automatic Parallelism

Xupeng Miao, Yujie Wang, Youhe Jiang, Chunan Shi, Xiaonan Nie, Hailin Zhang, Bin Cui

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Tags: Computer science, CUDA, Databases, Deep learning, nVidia, nVidia Titan RTX, Package

December 4, 2022 by hgpu

User-Driven Online Kernel Fusion for SYCL

Victor Perez, Lukas Sommer, Victor Lomüller, Kumudha Narasimhan, Mehdi Goli

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Tags: Compilers, Computer science, Deep learning, OpenCL, Package, SYCL

November 27, 2022 by hgpu

Hardware Checkpointing and Productive Debugging Flows for FPGAs

Sameh Attia

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Tags: Computer science, FPGA, Package, Thesis

November 20, 2022 by hgpu

Challenges and Techniques for Transparent Acceleration of Unmodified Big Data Applications

Maria Nektaria Xekalaki

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Tags: Benchmarking, Computer science, CUDA, FPGA, Heterogeneous systems, Java, nVidia, nVidia GeForce GTX 1060, nVidia Quadro GP100, OpenCL, Package, Performance, Tesla V100, Thesis

November 20, 2022 by hgpu

Going green: optimizing GPUs for energy efficiency through model-steered auto-tuning

Richard Schoonhoven, Bram Veenboer, Ben van Werkhoven, Kees Joost Batenburg

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Tags: Computer science, CUDA, Energy-efficient computing, nVidia, nVidia RTX A4000, OpenCL, Package, Tesla A100

November 20, 2022 by hgpu

TorchOpt: An Efficient Library for Differentiable Optimization

Jie Ren, Xidong Feng, Bo Liu, Xuehai Pan, Yao Fu, Luo Mai, Yaodong Yang

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Tags: Computer science, CUDA, Deep learning, Mathematical Software, nVidia, OpenMP, Package, PyTorch

November 20, 2022 by hgpu

Capturing the Memory Topology of GPUs

Dominik Größler

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia A100, nVidia GeForce 840 M, nVidia GeForce RTX 2080 Ti, nVidia Quadro P 6000, Package, Performance, PTX, Tesla K20, Tesla V100, Thesis

November 13, 2022 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

Packages

Portable C++ Code that can Look and Feel Like Fortran Code with Yet Another Kernel Launcher (YAKL)

Assessing Application Efficiency and Performance Portability in Single-Source Programming for Heterogeneous Parallel Systems

Precise Energy Consumption Measurements of Heterogeneous Artificial Intelligence Workloads

Three Contributions to the Theory and Practice of Optimizing Compilers

Galvatron: Efficient Transformer Training over Multiple GPUs Using Automatic Parallelism

User-Driven Online Kernel Fusion for SYCL

Hardware Checkpointing and Productive Debugging Flows for FPGAs

Challenges and Techniques for Transparent Acceleration of Unmodified Big Data Applications

Going green: optimizing GPUs for energy efficiency through model-steered auto-tuning

TorchOpt: An Efficient Library for Differentiable Optimization

Capturing the Memory Topology of 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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