high performance computing on graphics processing units: hgpu.org

hgpu.org » Computer science

LazyTensor: combining eager execution with domain-specific compilers

Alex Suhan, Davide Libenzi, Ailing Zhang, Parker Schuh, Brennan Saeta, Jie Young Sohn, Denys Shabalin

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Tags: Computer science, Deep learning, Package, Programming Languages, TPU

March 7, 2021 by hgpu

Benchmarking Modern Edge Devices for AI Applications

Pilsung Kang, Jongmin Jo

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Tags: Artificial intelligence, Benchmarking, Computer science, Deep learning, TPU

March 7, 2021 by hgpu

Integrating Accelerators in Heterogeneous Systems

Ján Veselý

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, Python, Thesis

March 7, 2021 by hgpu

Scalable communication for high-order stencil computations using CUDA-aware MPI

Johannes Pekkilä, Miikka S. Väisälä, Maarit J. Käpylä, Matthias Rheinhardt, Oskar Lappi

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Tags: Computer science, CUDA, Finite difference, Magnetohydrodynamics, MPI, nVidia, Tesla V100

March 7, 2021 by hgpu

Neural Network Libraries: A Deep Learning Framework Designed from Engineers’ Perspectives

Akio Hayakawa, Masato Ishii, Yoshiyuki Kobayashi, Akira Nakamura, Takuya Narihira, Yukio Obuchi, Andrew Shin, Takuya Yashima, Kazuki Yoshiyama

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Tags: Computer science, Deep learning, Neural networks, nVidia, nVidia DGX-1, Package, Tesla V100

March 7, 2021 by hgpu

Acceleration of Intrusion Detection in Encrypted Network Traffic Using Heterogeneous Hardware

Eva Papadogiannaki, Sotiris Ioannidis

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Tags: Computer science, Filtering, Heterogeneous systems, Intel UHD 630, nVidia, nVidia GeForce GTX 980, OpenCL, Security

February 28, 2021 by hgpu

GPU-aware Communication with UCX in Parallel Programming Models: Charm++, MPI, and Python

Jaemin Choi, Zane Fink, Sam White, Nitin Bhat, David F. Richards, Laxmikant V. Kale

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Tags: Benchmarking, Computer science, CUDA, Distributed computing, MPI, nVidia, Package, Python, Tesla V100

February 28, 2021 by hgpu

Using hardware performance counters to speed up autotuning convergence on GPUs

Jiří Filipovič, Jana Hozzová, Amin Nezarat, Jaroslav Oľha, Filip Petrovič

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Tags: Auto-Tuning, Computer science, CUDA, HPC, Machine learning, nVidia, nVidia GeForce GTX 1070, nVidia GeForce RTX 2080, OpenCL, Package, Performance, Vulkan

February 23, 2021 by hgpu

Optimal program variant generation for hybrid manycore systems

Cristian Urlea

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Tags: Computer science, Design space exploration, FPGA, Heterogeneous systems, OpenCL, performance portability, Thesis

February 21, 2021 by hgpu

A Newcomer In The PGAS World – UPC++ vs UPC: A Comparative Study

Jérémie Lagravière, Johannes Langguth, Martina Prugger, Phuong H. Ha, Xing Cai

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Tags: Computer science, Differential equations, Distributed data structures, Intel Xeon Phi, Package

February 21, 2021 by hgpu

A Survey of Machine Learning for Computer Architecture and Systems

Nan Wu, Yuan Xie

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Tags: Computer science, Machine learning, Review, Security

February 21, 2021 by hgpu

Serverless Computing Strategies on Cloud Platforms

Diana María Naranjo Delgado

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Tags: Cloud, Computer science, CUDA, Machine learning, nVidia, Tesla V100, Thesis, Virtualization

February 14, 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

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

LazyTensor: combining eager execution with domain-specific compilers

Benchmarking Modern Edge Devices for AI Applications

Integrating Accelerators in Heterogeneous Systems

Scalable communication for high-order stencil computations using CUDA-aware MPI

Neural Network Libraries: A Deep Learning Framework Designed from Engineers’ Perspectives

Acceleration of Intrusion Detection in Encrypted Network Traffic Using Heterogeneous Hardware

GPU-aware Communication with UCX in Parallel Programming Models: Charm++, MPI, and Python

Using hardware performance counters to speed up autotuning convergence on GPUs

Optimal program variant generation for hybrid manycore systems

A Newcomer In The PGAS World – UPC++ vs UPC: A Comparative Study

A Survey of Machine Learning for Computer Architecture and Systems

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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