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SLATE port to AMD and Intel platforms

Ahmad Abdelfattah, Mohammed Al Farhan, Cade Brown, Mark Gates, Dalal Sukkari, Asim YarKhan, Jack Dongarra

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Tags: Computer science, CUBLAS, CUDA, Linear Algebra, nVidia, OpenCL, Package, SYCL

April 18, 2021 by hgpu

Parallel Arbitrary-precision Integer Arithmetic

Davood Mohajerani

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Tags: Algorithms, Computer science, CUDA, nVidia, OpenCL, Package, Thesis

April 5, 2021 by hgpu

Accelerating AutoDock4 with GPUs and Gradient-Based Local Search

Diogo Santos-Martins, Leonardo Solis-Vasquez, Andreas Koch, and Stefano Forli

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Tags: Algorithms, Chemistry, Macromolecule, molecular docking, OpenCL, Package, Physics

February 28, 2021 by hgpu

Using hardware performance counters to speed up autotuning convergence on GPUs

Jiří Filipovič, Jana Hozzová, Amin Nezarat, Jaroslav Oľha, Filip Petrovič

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Tags: Auto-Tuning, Computer science, CUDA, HPC, Machine learning, nVidia, nVidia GeForce GTX 1070, nVidia GeForce RTX 2080, OpenCL, Package, Performance, Vulkan

February 23, 2021 by hgpu

Transparent FPGA Acceleration with TensorFlow

Simon Pfenning, Philipp Holzinger, Marc Reichenbach

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Tags: Computer science, Deep learning, FPGA, Hardware Architecture, HSA, OpenCL, Package, TensorFlow

February 14, 2021 by hgpu

C-for-Metal: High Performance SIMD Programming on Intel GPUs

Guei-Yuan Lueh, Kaiyu Chen, Gang Chen, Joel Fuentes, Wei-Yu Chen, Fangwen Fu, Hong Jiang, Hongzheng Li, Daniel Rhee

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Tags: Computer science, CUDA, nVidia, OpenCL, Package, Programming Languages

January 31, 2021 by hgpu

StencilFlow: Mapping Large Stencil Programs to Distributed Spatial Computing Systems

Johannes de Fine Licht, Andreas Kuster, Tiziano De Matteis, Tal Ben-Nun, Dominic Hofer, Torsten Hoefler

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Tags: Code generation, Computer science, CUDA, Distributed computing, FPGA, Heterogeneous systems, nVidia, OpenCL, Package, Tesla P100, Tesla V100

January 24, 2021 by hgpu

linus: Conveniently explore, share, and present large-scale biological trajectory data from a web browser

Johannes Waschke, Mario Hlawitschka, Kerim Anlas, Vikas Trivedi, Ingo Roeder, Jan Huisken, Nico Scherf

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Tags: Biology, OpenCL, Package, PyOpenCL, Python

January 10, 2021 by hgpu

Interactive Parallelization of C Programs in SAPFOR

Nikita Kataev

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Tags: Computer science, CUDA, LLVM, nVidia, nVidia GeForce GTX 1660 Ti, OpenCL, Package, Performance

January 3, 2021 by hgpu

Highly Efficient Lattice-Boltzmann Multiphase Simulations of Immiscible Fluids at High-Density Ratios on CPUs and GPUs through Code Generation

Markus Holzer, Martin Bauer, Harald Kostler, Ulrich Rüde

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Tags: Algorithms, cfd, Code generation, CUDA, Fluid dynamics, Lattice Boltzmann model, MPI, nVidia, OpenCL, Package, Physics, Programming techniques, Tesla P100, Tesla V100

December 20, 2020 by hgpu

Systolic-CNN: An OpenCL-defined Scalable Run-time-flexible FPGA Accelerator Architecture for Accelerating Convolutional Neural Network Inference in Cloud/Edge Computing

Akshay Dua, Yixing Li, Fengbo Ren

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Tags: Cloud, Computer science, DSP, FPGA, Hardware Architecture, Neural networks, OpenCL, Package

December 13, 2020 by hgpu

Evaluating the Performance and Portability of Contemporary SYCL Implementations

Beau Johnston, Jeffrey S. Vetter, Josh Milthorpe

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Tags: AMD Radeon VII, ATI, Benchmarking, Computer science, CUDA, Heterogeneous systems, HIP, Matrix multiplication, nVidia, OpenCL, Package, Performance, Tesla P100

November 29, 2020 by hgpu

IntelliKit: Agent-first tooling for AMD hardware

Kerncap: Automated Kernel Extraction and Isolation for AMD GPUs

DITRON: Distributed Compiler based on Triton for Parallel Systems

DITRON: Distributed Multi-level Tiling Compiler for Parallel Tensor Programs

CuTile Benchmark Suite: Performance and Productivity Tradeoffs for GPU Kernel Programming on Blackwell Architecture

Evaluating CUDA Tile for AI Workloads on Hopper and Blackwell GPUs

Agentic Code Optimization via Compiler-LLM Cooperation

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

DVM: Real-Time Kernel Generation for Dynamic AI Models

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

AutoKernel: Autonomous GPU Kernel Optimization via Iterative Agent-Driven Search

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

LLM.Q: Quantized LLM training in pure CUDA/C++

LLMQ: Efficient Lower-Precision LLM Training for Consumer GPUs

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

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high performance computing on graphics processing units: hgpu.org

Programming

SLATE port to AMD and Intel platforms

Parallel Arbitrary-precision Integer Arithmetic

Accelerating AutoDock4 with GPUs and Gradient-Based Local Search

Using hardware performance counters to speed up autotuning convergence on GPUs

Transparent FPGA Acceleration with TensorFlow

C-for-Metal: High Performance SIMD Programming on Intel GPUs

StencilFlow: Mapping Large Stencil Programs to Distributed Spatial Computing Systems

linus: Conveniently explore, share, and present large-scale biological trajectory data from a web browser

Interactive Parallelization of C Programs in SAPFOR

Highly Efficient Lattice-Boltzmann Multiphase Simulations of Immiscible Fluids at High-Density Ratios on CPUs and GPUs through Code Generation

Evaluating the Performance and Portability of Contemporary SYCL Implementations

Recent source codes

IntelliKit: Agent-first tooling for AMD hardware

DITRON: Distributed Compiler based on Triton for Parallel Systems

CuTile Benchmark Suite: Performance and Productivity Tradeoffs for GPU Kernel Programming on Blackwell Architecture

Agentic Code Optimization via Compiler-LLM Cooperation

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

LLM.Q: Quantized LLM training in pure CUDA/C++

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

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