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Exploiting dynamic sparse matrices for performance portable linear algebra operations

Chris Stylianou, Michele Weiland

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Tags: Computer science, CUDA, Heterogeneous systems, HPC, Linear Algebra, MPI, nVidia, OpenMP, Package, Sparse matrix, Tesla V100

October 2, 2022 by hgpu

EnergonAI: An Inference System for 10-100 Billion Parameter Transformer Models

Jiangsu Du, Ziming Liu, Jiarui Fang, Shenggui Li, Yongbin Li, Yutong Lu, Yang You

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Tags: Computer science, CUDA, Deep learning, Heterogeneous systems, NLP, nVidia, nVidia A100, Package

September 11, 2022 by hgpu

Towards Understanding and Mitigating Memory-Access Challenges in Computing Systems

Mohammad K. Dashti

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Tags: Computer science, CUDA, Deep learning, Heterogeneous systems, Memory model, nVidia, nVidia GeForce RTX 2070, nVidia Jetson TK1, OpenCL, Package, Thesis

August 28, 2022 by hgpu

Deep Learning Approaches to Source Code Analysis for Optimization of Heterogeneous Systems: Recent Results, Challenges and Opportunities

Francesco Barchi, Emanuele Parisi, Andrea Bartolini, Andrea Acquaviva

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Tags: AMD Radeon HD 7970, ATI, Computer science, Deep learning, Heterogeneous systems, nVidia, nVidia GeForce GTX 970, OpenCL, Optimization, Overview, Security

August 7, 2022 by hgpu

Design and Implementation of ShenWei Universal C/C++

Huanqi Cao, Jiajie Chen

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Tags: Compilers, Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL

August 7, 2022 by hgpu

Demystifying Dependency Bugs in Deep Learning Stack

Kaifeng Huang, Bihuan Chen, Susheng Wu, Junmin Cao, Lei Ma, Xin Peng

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Tags: Computer science, CUDA, Deep learning, Heterogeneous systems, nVidia

July 24, 2022 by hgpu

CPU-GPU Layer-Switched Low Latency CNN Inference

Ehsan Aghapour, Dolly Sapra, Andy Pimentel, and Anuj Pathania

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Tags: ARM, Computer science, Heterogeneous systems, Machine learning, Neural networks, OpenCL, Package, SoC

July 24, 2022 by hgpu

Heterogeneous Energy-aware Load Balancing for Industry 4.0 and IoT Environments

Usman Ahmed, Jerry Chun-Wei Lin, Gautam Srivastava

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Tags: Computer science, Heterogeneous systems, IoT, Machine learning, nVidia, nVidia GeForce GTX 760, nVidia GeForce GTX 860, OpenCL

July 17, 2022 by hgpu

Novel Parallel Approaches to Efficiently Solve Spatial Problems on Heterogeneous CPU-GPU Systems

Andrés Jesús Sánchez Fernández

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Tags: Algorithms, Computer science, Databases, Heterogeneous systems, nVidia, nVidia GeForce GT 1030, nVidia GeForce GTX 1050 Ti, nVidia GeForce GTX 980, Thesis

July 3, 2022 by hgpu

Evaluation of Intel’s DPC++ Compatibility Tool in heterogeneous computing

Germán Castaño, Youssef Faqir-Rhazoui, Carlos García, Manuel Prieto-Matías

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Tags: Computer science, CUDA, FPGA, Heterogeneous systems, nVidia, Package, Performance, SYCL

July 3, 2022 by hgpu

Optimizing the Performance of Parallel and Concurrent Applications Based on Asynchronous Many-Task Runtimes

Weile Wei

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, nVidia, Optimization, Package, QMC, Tesla V100, Thesis

July 3, 2022 by hgpu

Tensor Computation Based on Heterogeneous Memory

Jiawen Liu

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Tags: Computer science, Heterogeneous systems, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 1080 Ti, OpenCL, Prefetch, Thesis

July 3, 2022 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

Exploiting dynamic sparse matrices for performance portable linear algebra operations

EnergonAI: An Inference System for 10-100 Billion Parameter Transformer Models

Towards Understanding and Mitigating Memory-Access Challenges in Computing Systems

Deep Learning Approaches to Source Code Analysis for Optimization of Heterogeneous Systems: Recent Results, Challenges and Opportunities

Design and Implementation of ShenWei Universal C/C++

Demystifying Dependency Bugs in Deep Learning Stack

CPU-GPU Layer-Switched Low Latency CNN Inference

Heterogeneous Energy-aware Load Balancing for Industry 4.0 and IoT Environments

Novel Parallel Approaches to Efficiently Solve Spatial Problems on Heterogeneous CPU-GPU Systems

Evaluation of Intel’s DPC++ Compatibility Tool in heterogeneous computing

Optimizing the Performance of Parallel and Concurrent Applications Based on Asynchronous Many-Task Runtimes

Tensor Computation Based on Heterogeneous Memory

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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