high performance computing on graphics processing units: hgpu.org

Massinissa Merouani, Khaled Afif Boudaoud, Iheb Nassim Aouadj, Nassim Tchoulak, Islam Kara Bernou, Hamza Benyamina, Fatima Benbouzid-Si Tayeb, Karima Benatchba, Hugh Leather, Riyadh Baghdadi

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Tags: Compilers, Computer science, Deep learning, Machine learning, OpenCL, Package, Programming Languages

March 24, 2024 by hgpu

APPy: Annotated Parallelism for Python on GPUs

Tong Zhou, Jun Shirako, Vivek Sarkar

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Tags: Code generation, Compilers, Computer science, CUDA, Machine learning, nVidia, nVidia GeForce RTX 3090, Python

February 25, 2024 by hgpu

TransAxx: Efficient Transformers with Approximate Computing

Dimitrios Danopoulos, Georgios Zervakis, Dimitrios Soudris, Jörg Henkel

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Tags: Computer science, CUDA, Machine learning, Neural networks, nVidia, nVidia V100, Package, PyTorch

February 18, 2024 by hgpu

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

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

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

Agentic Code Optimization via Compiler-LLM Cooperation

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

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

LLMQ: Efficient Lower-Precision LLM Training for Consumer GPUs

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high performance computing on graphics processing units: hgpu.org

COOK Access Control on an embedded Volta GPU

Fast and Practical Strassen’s Matrix Multiplication using FPGAs

Machine learning enhanced code optimization for high-level synthesis (ML-ECOHS)

Addressing Challenges in Utilizing GPUs for Accelerating Privacy-Preserving Computation

STuning-DL: Model-Driven Autotuning of Sparse GPU Kernels for Deep Learning

Hierarchical Resource Partitioning on Modern GPUs: A Reinforcement Learning Approach

A Systematic Literature Survey of Sparse Matrix-Vector Multiplication

QArray: a GPU-accelerated constant capacitance model simulator for large quantum dot arrays

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

APPy: Annotated Parallelism for Python on GPUs

TransAxx: Efficient Transformers with Approximate Computing

Recent source codes

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

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

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

Device Virtual Machine (DVM)

Agentic Code Optimization via Compiler-LLM Cooperation

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

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