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Thwarting Piracy: Anti-debugging Using GPU-assisted Self-healing Codes

Adhokshaj Mishra, Manjesh Kumar Hanawal

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 960 M, Security

October 23, 2022 by hgpu

Behavioral graph fraud detection in E-commerce

Hang Yin, Zitao Zhang, Zhurong Wang, Yilmazcan Ozyurt, Weiming Liang, Wenyu Dong, Yang Zhao, Yinan Shan

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Tags: Clustering, Finance, Machine learning, Neural networks, Security

October 23, 2022 by hgpu

From Task-Based GPU Work Aggregation to Stellar Mergers: Turning Fine-Grained CPU Tasks into Portable GPU Kernels

Gregor Daiß, Patrick Diehl, Dominic Marcello, Alireza Kheirkhahan, Hartmut Kaiser, Dirk Pflüger

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Tags: AMD Radeon Instinct MI100, Astrophysics, ATI, CUDA, HIP, nVidia, nVidia A100, Package, performance portability, Physics

October 23, 2022 by hgpu

Dataloader Parameter Tuner: An Automated Dataloader Parameter Tuner for Deep Learning Models

JooYoung Park, DoangJoo Synn, XinYu Piao, Jong-Kook Kim

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Tags: Computer science, CUDA, Deep learning, Machine learning, nVidia, nVidia GeForce RTX 3080 Ti, Performance, Prefetch

October 16, 2022 by hgpu

OpenMP Offloading in the Jetson Nano Platform

Ilias K. Kasmeridis, Vassilios V. Dimakopoulos

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Tags: Compilers, Computer science, CUDA, nVidia, nVidia Jetson Nano, OpenMP, Package

October 16, 2022 by hgpu

Distributed, combined CPU and GPU profiling within HPX using APEX

Patrick Diehl, Gregor Daiss, Kevin Huck, Dominic Marcello, Sagiv Shiber, Hartmut Kaiser, Juhan Frank, Geoffrey C. Clayton, Dirk Pflueger

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Tags: Astrophysics, Benchmarking, Computer science, CUDA, nVidia, Package, Tesla P100, Tesla V100

October 16, 2022 by hgpu

PMT: Power Measurement Toolkit

Stefano Corda, Bram Veenboer, Emma Tolley

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Tags: AMD Radeon Pro W6600, ATI, Computer science, Energy-efficient computing, HPC, nVidia, nVidia Titan RTX, Package, Performance

October 16, 2022 by hgpu

Bottleneck Analysis of Dynamic Graph Neural Network Inference on CPU and GPU

Hanqiu Chen, Yahya Alhinai, Yihan Jiang, Eunjee Na, Cong Hao

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Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia RTX A6000, Package

October 16, 2022 by hgpu

Towards Performance Portable Programming for Distributed Heterogeneous Systems

Polykarpos Thomadakis, Nikos Chrisochoides

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL, Package, Performance, performance portability, Tesla V100

October 9, 2022 by hgpu

Decompiling x86 Deep Neural Network Executables

Zhibo Liu, Yuanyuan Yuan, Shuai Wang, Xiaofei Xie, Lei Ma

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Tags: Computer science, CUDA, Deep learning, Heterogeneous systems, Neural networks, nVidia, nVidia GeForce RTX 2080, OpenCL, Package, Security

October 9, 2022 by hgpu

Benchmarking optimization algorithms for auto-tuning GPU kernels

Richard Schoonhoven, Ben van Werkhoven, Kees Joost Batenburg

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Tags: AMD Radeon Instinct Mi50, ATI, Auto-Tuning, Benchmarking, Computer science, CUDA, nVidia, nVidia A100, nVidia GeForce GTX 1080 Ti, nVidia GeForce GTX Titan X, nVidia Titan RTX, OpenCL, Performance, pyCUDA, PyOpenCL, Tesla K20, Tesla P100, Tesla V100

October 9, 2022 by hgpu

Performance portability study of epistasis detection using SYCL on NVIDIA GPU

Zheming Jin, Jeffrey S. Vetter

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Tags: Computer science, CUDA, Genomics, nVidia, performance portability, SYCL, Tesla V100

October 9, 2022 by hgpu

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

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

Thwarting Piracy: Anti-debugging Using GPU-assisted Self-healing Codes

Behavioral graph fraud detection in E-commerce

From Task-Based GPU Work Aggregation to Stellar Mergers: Turning Fine-Grained CPU Tasks into Portable GPU Kernels

Dataloader Parameter Tuner: An Automated Dataloader Parameter Tuner for Deep Learning Models

OpenMP Offloading in the Jetson Nano Platform

Distributed, combined CPU and GPU profiling within HPX using APEX

PMT: Power Measurement Toolkit

Bottleneck Analysis of Dynamic Graph Neural Network Inference on CPU and GPU

Towards Performance Portable Programming for Distributed Heterogeneous Systems

Decompiling x86 Deep Neural Network Executables

Benchmarking optimization algorithms for auto-tuning GPU kernels

Performance portability study of epistasis detection using SYCL on NVIDIA GPU

Recent source codes

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

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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