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The GPUVerify Method: a Tutorial Overview

Alastair F. Donaldson

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Tags: Computer science, CUDA, nVidia, OpenCL, Overview, Package, Programming techniques, Tutorial

September 16, 2014 by hgpu

Convex Clustering: An Attractive Alternative to Hierarchical Clustering

Gary K. Chen, Eric Chi, John Ranola, Kenneth Lange

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Tags: Algorithms, Bioinformatics, Biology, Cluster analysis, Clustering, Genomics, Hierarchical clustering, nVidia, OpenCL, Package, Tesla C2050

September 9, 2014 by hgpu

Heterogeneous Computing on Mixed Unstructured Grids with PyFR

F. D. Witherden, B. C. Vermeire, P. E. Vincent

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Tags: AMD FirePro W9100, ATI, CUDA, Fluid dynamics, Heterogeneous systems, nVidia, OpenCL, Package, Tesla K40

September 2, 2014 by hgpu

A Domain Specific Approach to Heterogeneous Computing: From Availability to Accessibility

Gordon Inggs, David Thomas, Wayne Luk

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Tags: Computational finance, Finance, FPGA, Heterogeneous systems, OpenCL, Option pricing, Package, Python

August 23, 2014 by hgpu

GPU-SPARC: Accelerating Parallelism in Multi-GPU Real-Time Systems

Wookhyun Han, Hwidong Bae, Hyosu Kim, Jiyoen Lee, Insik Shin

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Tags: Computer science, nVidia, nVidia GeForce GTX 560 Ti, OpenCL, Package, Performance

August 13, 2014 by hgpu

Optimizing memory management on heterogeneous systems using polyhedral, compile-time techniques

Vassilis Vassiliadis

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Tags: Computer science, Heterogeneous systems, Memory model, OpenCL, Package, Thesis

August 7, 2014 by hgpu

Bayesian model comparison via sequential Monte Carlo

Yan Zhou

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Tags: Algorithms, Bayesian, Mathematics, nVidia, nVidia Quadro 2000, OpenCL, Package, Statistics, Thesis

July 28, 2014 by hgpu

Utilizing state-of-art NeuroES and GPGPU to optimize Mario AI

Hasse Lovgren

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Tags: ATI, ATI Radeon HD 5870, Computer science, Mathematics, Neural networks, nVidia, nVidia GeForce GTX 580, OpenCL, Package, Performance, Tesla C1060, Thesis

July 9, 2014 by hgpu

GiMMiK – Generating Bespoke Matrix Multiplication Kernels for Various Hardware Accelerators; Applications in High-Order Computational Fluid Dynamics

Bartosz D. Wozniak

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Tags: CUBLAS, CUDA, Fluid dynamics, Linear Algebra, Matrix multiplication, nVidia, OpenCL, Package, Python, Tesla K40, Thesis

July 7, 2014 by hgpu

Writing self-adaptive codes for heterogeneous systems

Jorge F. Fabeiro, Diego Andrade, Basilio B. Fraguela, Ramon Doallo

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Tags: Computer science, Heterogeneous systems, Intel Xeon Phi, nVidia, OpenCL, Package, Tesla K20

July 4, 2014 by hgpu

On the Characterization of OpenCL Dwarfs on Fixed and Reconfigurable Platforms

Konstantinos Krommydas, Wu-chun Feng, Muhsen Owaida, Christos D. Antonopoulos, Nikolaos Bellas

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Tags: APU, ATI, ATI Radeon HD 7970, Benchmarking, Computer science, FPGA, Heterogeneous systems, Intel Xeon Phi, OpenCL, Package, Performance

June 26, 2014 by hgpu

Using JavaScript and WebCL for Numerical Computations: A Comparative Study of Native and Web Technologies

Faiz Khan, Vincent Foley-Bourgon, Sujay Kathrotia, Erick Lavoie, Laurie Hendren

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Tags: ATI, ATI Radeon HD 7970, Benchmarking, Computer science, Java, nVidia, OpenCL, Package, Tesla C2050

June 23, 2014 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

The GPUVerify Method: a Tutorial Overview

Convex Clustering: An Attractive Alternative to Hierarchical Clustering

Heterogeneous Computing on Mixed Unstructured Grids with PyFR

A Domain Specific Approach to Heterogeneous Computing: From Availability to Accessibility

GPU-SPARC: Accelerating Parallelism in Multi-GPU Real-Time Systems

Optimizing memory management on heterogeneous systems using polyhedral, compile-time techniques

Bayesian model comparison via sequential Monte Carlo

Utilizing state-of-art NeuroES and GPGPU to optimize Mario AI

GiMMiK – Generating Bespoke Matrix Multiplication Kernels for Various Hardware Accelerators; Applications in High-Order Computational Fluid Dynamics

Writing self-adaptive codes for heterogeneous systems

On the Characterization of OpenCL Dwarfs on Fixed and Reconfigurable Platforms

Using JavaScript and WebCL for Numerical Computations: A Comparative Study of Native and Web Technologies

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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