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Efficiently Processing Large Relational Joins on GPUs

Bowen Wu, Dimitrios Koutsoukos, Gustavo Alonso

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Tags: Benchmarking, Computer science, CUDA, Databases, nVidia, nVidia A100, nVidia GeForce RTX 3090, Package

December 10, 2023 by hgpu

Testing and Mutation Testing for GPU Kernels

Yujie Liu

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Tags: Benchmarking, Computer science, CUDA, nVidia, Package, Performance, Thesis

December 3, 2023 by hgpu

CuPBoP-AMD: Extending CUDA to AMD Platforms

Jun Chen, Xule Zhou, Hyesoon Kim

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Tags: AMD Radeon Instinct MI210, ATI, Code generation, Computer science, CUDA, HIP, nVidia, OpenCL, Package

December 3, 2023 by hgpu

Evaluation of FPGA-based high performance computing platforms

Martin Frick-Lundgren

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Tags: Computer science, FPGA, HPC, Intel, Intel UHD 630, Matrix multiplication, OpenCL, Package, SYCL, Thesis

November 27, 2023 by hgpu

Creating a Dataset for High-Performance Computing Code Translation using LLMs: A Bridge Between OpenMP Fortran and C+

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

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Tags: Benchmarking, Computer science, CUDA, Fortran, Machine learning, nVidia, nVidia RTX A6000, OpenMP, Package

November 19, 2023 by hgpu

CHARM-SYCL: New Unified Programming Environment for Multiple Accelerator Types

Norihisa Fujita, Beau Johnston, Ryohei Kobayashi, Keita Teranishi, Seyong Lee, Taisuke Boku, Jeffrey S. Vetter

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Tags: AMD Radeon Instinct MI100, ATI, Computer science, CUDA, Heterogeneous systems, nVidia, Package, performance portability, SYCL, Tesla V100

November 19, 2023 by hgpu

AFOCL: Portable OpenCL Programming of FPGAs via Automated Built-in Kernel Management

Topi Leppanen, Joonas Multanen, Leevi Leppänen, Pekka Jääskeläinen

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Tags: Computer science, FPGA, OpenCL, Package, performance portability

November 19, 2023 by hgpu

An approach to performance portability through generic programming

Andreas Hadjigeorgiou, Christodoulos Stylianou, Michele Weiland, Dirk Jacob Verschuur, Jacob Finkenrath

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Tags: AMD Radeon Instinct MI100, ATI, Benchmarking, Computer science, CUDA, HIP, HPC, nVidia, OpenMP, Package, Performance, performance portability, Portability, Tesla V100

November 12, 2023 by hgpu

Redco: A Lightweight Tool to Automate Distributed Training of LLMs on Any GPU/TPUs

Bowen Tan, Yun Zhu, Lijuan Liu, Hongyi Wang, Yonghao Zhuang, Jindong Chen, Eric Xing, Zhiting Hu

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Tags: Computer science, Deep learning, Distributed computing, nVidia, nVidia A100, Package, TPU

November 5, 2023 by hgpu

OpenRAND: A Performance Portable, Reproducible Random Number Generation Library for Parallel Computations

Shihab Shahriar Khan, Bryce Palmer, Christopher Edelmaierd, Hasan Metin Aktulga

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Tags: Computer science, CUDA, HPC, nVidia, nVidia A100, Package, Pseudo-random number generators, Tesla V100

November 5, 2023 by hgpu

Performance portability evaluation of blocked stencil computations on GPUs

Oscar Antepara, Hans Johansen, Samuel Williams, Tuowen Zhao, Samantha Hirsch, Priya Goyal, Mary Hall

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Tags: AMD Radeon Instinct MI250X, ATI, Code generation, Computer science, CUDA, HIP, nVidia, nVidia A100, Package, performance portability, Stencil computation, SYCL

October 29, 2023 by hgpu

SYCL in the Edge: Performance Evaluation for Heterogeneous Acceleration

Youssef Faqir-Rhazoui, Carlos García

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Tags: Computer science, CUDA, FPGA, Heterogeneous systems, nVidia, nVidia Jetson Nano, Optical flow, Package, SYCL

October 22, 2023 by hgpu

Specx: Speculative task-based runtime system

Specx: a C++ task-based runtime system for heterogeneous distributed architectures

Mutual-Supervised Learning for Sequential-to-Parallel Code Translation

KISim: Kubernetes Intelligent Scheduling Simulator

KIS-S: A GPU-Aware Kubernetes Inference Simulator with RL-Based Auto-Scaling

Hardware Compute Partitioning on NVIDIA GPUs for Composable Systems

Efficient GPU Implementation of Multi-Precision Integer Division

ParEval: A Parallel Code Evaluation Benchmark

ParEval-Repo: A Benchmark Suite for Evaluating LLMs with Repository-level HPC Translation Tasks

FlashSparse: Minimizing Computation Redundancy for Fast Sparse Matrix Multiplications on Tensor Cores

Libra: Synergizing CUDA and Tensor Cores for High-Performance Sparse Matrix Multiplication

exa-AMD: Exascale Accelerated Materials Discovery

Accelerated discovery and design of Fe-Co-Zr magnets with tunable magnetic anisotropy through machine learning and parallel computing

WiLLM: An Open Wireless LLM Communication System

Vcc: the Vulkan Clang Compiler

No More Shading Languages: Compiling C++ to Vulkan Shaders

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high performance computing on graphics processing units: hgpu.org

Applications

Efficiently Processing Large Relational Joins on GPUs

Testing and Mutation Testing for GPU Kernels

CuPBoP-AMD: Extending CUDA to AMD Platforms

Evaluation of FPGA-based high performance computing platforms

Creating a Dataset for High-Performance Computing Code Translation using LLMs: A Bridge Between OpenMP Fortran and C+

CHARM-SYCL: New Unified Programming Environment for Multiple Accelerator Types

AFOCL: Portable OpenCL Programming of FPGAs via Automated Built-in Kernel Management

An approach to performance portability through generic programming

Redco: A Lightweight Tool to Automate Distributed Training of LLMs on Any GPU/TPUs

OpenRAND: A Performance Portable, Reproducible Random Number Generation Library for Parallel Computations

Performance portability evaluation of blocked stencil computations on GPUs

SYCL in the Edge: Performance Evaluation for Heterogeneous Acceleration

Recent source codes

Specx: Speculative task-based runtime system

Mutual-Supervised Learning for Sequential-to-Parallel Code Translation

KISim: Kubernetes Intelligent Scheduling Simulator

Hardware Compute Partitioning on NVIDIA GPUs for Composable Systems

Efficient GPU Implementation of Multi-Precision Integer Division

ParEval: A Parallel Code Evaluation Benchmark

FlashSparse: Minimizing Computation Redundancy for Fast Sparse Matrix Multiplications on Tensor Cores

exa-AMD: Exascale Accelerated Materials Discovery

WiLLM: An Open Wireless LLM Communication System

Vcc: the Vulkan Clang Compiler

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