high performance computing on graphics processing units: hgpu.org

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Reducing the Cost of Heuristic Generation with Machine Learning

William F. Ogilvie

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Tags: Computer science, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce GTX Titan, OpenCL, Thesis

July 1, 2018 by hgpu

Compiler Fuzzing through Deep Learning

Chris Cummins, Pavlos Petoumenos, Hugh Leather, Alastair Murray

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Tags: Compilers, Computer science, Deep learning, Intel Xeon Phi, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX 780, OpenCL, Package

July 1, 2018 by hgpu

Introducing Parallelism to the Ranges TS

Gordon Brown, Christopher Di Bella, Michael Haidl, Toomas Remmelg, Ruyman Reyes, Michel Steuwer

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Tags: Computer science, Heterogeneous systems, OpenCL, Package, SYCL

June 28, 2018 by hgpu

Analyzing Memory Accesses for Performance and Correctness of Parallel Programs

Tim Cramer

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Tags: Computer science, Heterogeneous systems, HPC, Intel Xeon Phi, Memory model, OpenMP, Parallel programming, Thesis

June 28, 2018 by hgpu

Migrating from OpenGL ES to Vulkan

Imagination Technologies Limited

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Tags: 3D Graphics and Realism, OpenGL, Rendering, Vulkan

June 28, 2018 by hgpu

Improving tasks throughput on accelerators using OpenCL command concurrency

A.J. Lazaro-Munoz, J.M. Gonzalez-Linares, J. Gomez-Luna, N. Guil

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Tags: AMD Radeon R9, ATI, Computer science, Heterogeneous systems, Intel Xeon Phi, nVidia, OpenCL, Performance, Task scheduling, Tesla K20

June 28, 2018 by hgpu

Go game move prediction using convolutional neural network

Marek Korenciak

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Tags: Caffe, CNN, Computer science, CUDA, Deep learning, Games, nVidia, nVidia GeForce GTX 1080 Ti, Package, Thesis

June 24, 2018 by hgpu

Synthesis of GPU Programs from High-Level Models

Ziyuan Jiang

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Tags: AMD FirePro S7150, ATI, Computer science, OpenCL, programming model, SoC, Thesis

June 24, 2018 by hgpu

DeepSmith: Compiler Fuzzing through Deep Learning

Chris Cummins, Pavlos Petoumenos, Alastair Murray, Hugh Leather

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Tags: Compilers, Computer science, Deep learning, nVidia, OpenCL, Package, Tesla K40

June 24, 2018 by hgpu

Energy Efficient Computing on Multi-core Processors: Vectorization and Compression Techniques

Abdullah Al Hasib

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Tags: Compression, Computer science, Data parallelism, Intel Xeon Phi, Prefetch, Programming techniques, Thesis

June 20, 2018 by hgpu

RAPIDNN: In-Memory Deep Neural Network Acceleration Framework

Mohsen Imani, Mohammad Samragh, Yeseong Kim, Saransh Gupta, Farinaz Koushanfar, Tajana Rosing

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Tags: AMD Radeon HD 7970, ATI, CNN, Computer science, Deep learning, FPGA, Hardware Architecture, Neural networks, OpenCL

June 20, 2018 by hgpu

Comparing Two Generations of Embedded GPUs Running a Feature Detection Algorithm

Max Danielsson, Hakan Grahn, Thomas Sievert, Jim Rasmusson

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Tags: Computer science, Computer vision, OpenCL

June 20, 2018 by hgpu

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

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

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

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high performance computing on graphics processing units: hgpu.org

Applications

Reducing the Cost of Heuristic Generation with Machine Learning

Compiler Fuzzing through Deep Learning

Introducing Parallelism to the Ranges TS

Analyzing Memory Accesses for Performance and Correctness of Parallel Programs

Migrating from OpenGL ES to Vulkan

Improving tasks throughput on accelerators using OpenCL command concurrency

Go game move prediction using convolutional neural network

Synthesis of GPU Programs from High-Level Models

DeepSmith: Compiler Fuzzing through Deep Learning

Energy Efficient Computing on Multi-core Processors: Vectorization and Compression Techniques

RAPIDNN: In-Memory Deep Neural Network Acceleration Framework

Comparing Two Generations of Embedded GPUs Running a Feature Detection Algorithm

Recent source codes

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

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

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

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

True 4-Bit Quantized CNN Training on CPU

cuFuzz: A GPU-oriented coverage-guided fuzzer for userland CUDA application

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

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