high performance computing on graphics processing units: hgpu.org

Nicolò Ghielmetti, Vladimir Loncar, Maurizio Pierini, Marcel Roed, Sioni Summers, Thea Aarrestad, Christoffer Petersson, Hampus Linander, Jennifer Ngadiuba, Kelvin Lin, Philip Harris

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Tags: Computer science, Deep learning, FPGA, Heterogeneous systems, HLS, Machine learning, Neural networks, Package

May 22, 2022 by hgpu

AnySeq/GPU: A Novel Approach for Faster Sequence Alignment on GPUs

André Müller, Bertil Schmidt, Richard Membarth, Roland Leißa, Sebastian Hack

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Tags: AMD Radeon Instinct MI100, ATI, Bioinformatics, Biology, CUDA, Next-Generation sequencing, nVidia, nVidia GeForce RTX 3090, OpenCL, Package, Sequence alignment, Tesla A100

May 22, 2022 by hgpu

GPU-based JSON data processing using structural indexes

Koen R. Vlaswinkel

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Tags: Computer science, CUDA, Java, nVidia, nVidia GeForce GTX 1080, Package, Python, Thesis

May 15, 2022 by hgpu

Experience of Migrating a Parallel Graph Coloring Program from CUDA to SYCL

Zheming Jin

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Tags: Benchmarking, Computer science, CUDA, Graph theory, nVidia, OpenCL, Package, Performance, SYCL

May 8, 2022 by hgpu

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

True 4-Bit Quantized CNN Training on CPU

True 4-Bit Quantized Convolutional Neural Network Training on CPU: Achieving Full-Precision Parity

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

Hunting CUDA Bugs at Scale with cuFuzz

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

MSKernelBench & CUDAMaster

Making LLMs Optimize Multi-Scenario CUDA Kernels Like Experts

EvoScientist: Harness Vibe Research with Self-evolving AI Scientists

EvoScientist: Towards Multi-Agent Evolving AI Scientists for End-to-End Scientific Discovery

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build & Test Pipeline of Code Repositories on ANY Language and ANY Platform

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high performance computing on graphics processing units: hgpu.org

Applications

On the Compilation Performance of Current SYCL Implementations

Seamless GPU acceleration for C++ based physics with the Metal Shading Language on Apple’s M series unified chips

Onesweep: A Faster Least Significant Digit Radix Sort for GPUs

FELARE: Fair Scheduling of Machine Learning Applications on Heterogeneous Edge Systems

Fast GPU bounding boxes on tree-structured scenes

Lossless Acceleration for Seq2seq Generation with Aggressive Decoding

Real-time semantic segmentation on FPGAs for autonomous vehicles with hls4ml

AnySeq/GPU: A Novel Approach for Faster Sequence Alignment on GPUs

GPU-based JSON data processing using structural indexes

Experience of Migrating a Parallel Graph Coloring Program from CUDA to SYCL

Recent source codes

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

MSKernelBench & CUDAMaster

EvoScientist: Harness Vibe Research with Self-evolving AI Scientists

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

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