high performance computing on graphics processing units: hgpu.org

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Manas: Mining Software Repositories to Assist AutoML

Giang Nguyen, Johir Islam, Rangeet Pan, Hridesh Rajan

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Tags: Computer science, Deep learning, Neural networks, nVidia, Package, Python, Software Engineering, Tesla V100

December 12, 2021 by hgpu

Fast Neural Representations for Direct Volume Rendering

Sebastian Weiss, Philipp Hermüller, Rüdiger Westermann

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Tags: 3D Graphics and Realism, CUDA, GLSL, nVidia, nVidia GeForce RTX 2070, Package, Raytracing, Rendering, Visualization

December 12, 2021 by hgpu

CitiusSynapse: A Deep Learning Framework for Embedded Systems

Seungtae Hong, Hyunwoo Cho, Jeong-Si Kim

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Tags: Computer science, Deep learning, Embedded high-performance computing, OpenCL

December 12, 2021 by hgpu

GPU backed Data Mining on Android Devices

Robert Fritze, Claudia Plant

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Tags: Android, Computer science, Data mining, HPC, Machine learning, OpenCL, Package

December 12, 2021 by hgpu

High performance computing on Android devices – a case study

Robert Fritze, Claudia Plant

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Tags: AMD Radeon VII, Android, ATI, Benchmarking, Computer science, HPC, nVidia GeForce Titan V, OpenCL

December 12, 2021 by hgpu

Analysis and Comparison of Performance and Power Consumption of Neural Networks on CPU, GPU, TPU and FPGA

Christopher Noel Hesse

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Tags: Benchmarking, Computer science, Deep learning, FPGA, Neural networks, nVidia, nVidia DGX-1, nVidia GeForce RTX 2070, nVidia Jetson Nano, Performance, Tesla V100

December 5, 2021 by hgpu

Autotuning CUDA: Applying NLP Techniques to LS-CAT

Lars Bjertnes, Jacob O. Tørring, Anne C. Elster

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Tags: Auto-Tuning, Computer science, CUDA, Machine learning, NLP, nVidia

December 5, 2021 by hgpu

An Auto-Programming Approach to Vulkan

Vladimir Frolov, Vadim Sanzharov, Vladimir Galaktionov, Alexandr Scherbakov

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Tags: 3D Graphics and Realism, Code generation, nVidia, nVidia GeForce RTX 2080, Raytracing, Vulkan

December 5, 2021 by hgpu

Bayesian Optimization for auto-tuning GPU kernels

Floris-Jan Willemsen, Rob van Nieuwpoort, Ben van Werkhoven

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Tags: Bayesian, Computer science, CUDA, Machine learning, nVidia, nVidia A100, nVidia GeForce GTX Titan X, nVidia GeForce RTX 2070, OpenCL, Package, Performance

December 5, 2021 by hgpu

Atos: A Task-Parallel GPU Dynamic Scheduling Framework for Dynamic Irregular Computations

Yuxin Chen, Benjamin Brock, Serban Porumbescu, Aydın Buluç, Katherine Yelick, John D. Owens

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Tags: Computer science, CUDA, nVidia, Task scheduling, Tesla V100

December 5, 2021 by hgpu

Concurrency Mapping to FPGAs with OpenCL: A Case Study with a Shallow Water Kernel

Moteb Alghamdi, Graham Riley, Mike Ashworth

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Tags: Computer science, FPGA, HLS, HPC, OpenCL

November 28, 2021 by hgpu

Generating GPU Compiler Heuristics using Reinforcement Learning

Ian Colbert, Jake Daly, Norm Rubin

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Tags: AMD Radeon 6800 XT, AMD Radeon RX 5700 XT, ATI, Compilers, Computer science, Machine learning, Performance

November 28, 2021 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

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

Applications

Manas: Mining Software Repositories to Assist AutoML

Fast Neural Representations for Direct Volume Rendering

CitiusSynapse: A Deep Learning Framework for Embedded Systems

GPU backed Data Mining on Android Devices

High performance computing on Android devices – a case study

Analysis and Comparison of Performance and Power Consumption of Neural Networks on CPU, GPU, TPU and FPGA

Autotuning CUDA: Applying NLP Techniques to LS-CAT

An Auto-Programming Approach to Vulkan

Bayesian Optimization for auto-tuning GPU kernels

Atos: A Task-Parallel GPU Dynamic Scheduling Framework for Dynamic Irregular Computations

Concurrency Mapping to FPGAs with OpenCL: A Case Study with a Shallow Water Kernel

Generating GPU Compiler Heuristics using Reinforcement Learning

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

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

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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