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Improving Resource Efficiency in Virtualized Datacenters

Marcelo Amaral

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Tags: Cloud, Computer science, CUDA, nVidia, nVidia DGX-1, Thesis, Virtualization

May 12, 2019 by hgpu

Workload-aware Automatic Parallelization for Multi-GPU DNN Training

Sungho Shin, Youngmin Jo, Jungwook Choi, Swagath Venkataramani, Vijayalakshmi Srinivasan, Wonyong Sung

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Tags: Artificial intelligence, Computer science, Deep learning, Neural networks, nVidia, nVidia DGX-1, nVidia GeForce GTX Titan XP, nVidia Tesla GP100

November 11, 2018 by hgpu

Synkhronos: a Multi-GPU Theano Extension for Data Parallelism

Adam Stooke, Pieter Abbeel

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Tags: Computer science, CUDA, Data parallelism, Deep learning, nVidia, nVidia DGX-1, Package, Tesla P100, Theano

October 15, 2017 by hgpu

Optimized Broadcast for Deep Learning Workloads on Dense-GPU InfiniBand Clusters: MPI or NCCL?

Ammar Ahmad Awan, Ching-Hsiang Chu, Hari Subramoni, Dhabaleswar K. Panda

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Tags: Computer science, CUDA, Deep learning, GPU cluster, MPI, nVidia, nVidia DGX-1, OpenMPI, Tesla V100

August 1, 2017 by hgpu

Optimizing Deep CNN-Based Queries over Video Streams at Scale

Daniel Kang, John Emmons, Firas Abuzaid, Peter Bailis, Matei Zaharia

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Tags: Computer science, Computer vision, Databases, Deep learning, Machine learning, Neural networks, nVidia, nVidia DGX-1, Package, TensorFlow, Tesla P100

March 9, 2017 by hgpu

Brian2 Atlas

A Unified Intermediate Representation and Execution Architecture for Heterogeneous and Distributed Neural Simulation

AgentGarten: Code Worlds for Evolving Agents

KernelLens: Automated Triton-to-C++ Compiler for Enterprise Inference Runtimes

KernelLens: Deploying Triton Kernels to C++ Inference Engines

KernelZero: Co-Evolving Proposer and Coder for Continuously Improved GPU Kernel Generation

Introduction to Computer Vision

ACT: Accelerator Compiler Toolkit

Automatically Generating ML Compiler Backends from Tensor Accelerator ISA Descriptions

pytest-gpu-proof: A Simple Pytest Extension for Local GPU Code Testing

pytest-gpu-proof: Enabling Cloud-CPU Continuous Integration for GPU Code with Local GPU Attestation

Accelerating the Mitigation of LLM Inference Nondeterminism Across GPU Architectures

AutoTuneBench: Trustworthy Measurement for Agent Auto-Tuning of LLM Serving Engines

nvsass-disassembler

Automated Instruction Encoding Synthesis for Modern GPU ISA Compression

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high performance computing on graphics processing units: hgpu.org

Workload-aware Automatic Parallelization for Multi-GPU DNN Training

Synkhronos: a Multi-GPU Theano Extension for Data Parallelism

Optimized Broadcast for Deep Learning Workloads on Dense-GPU InfiniBand Clusters: MPI or NCCL?

Optimizing Deep CNN-Based Queries over Video Streams at Scale

Recent source codes

Brian2 Atlas

AgentGarten: Code Worlds for Evolving Agents

KernelLens: Automated Triton-to-C++ Compiler for Enterprise Inference Runtimes

KernelZero: Co-Evolving Proposer and Coder for Continuously Improved GPU Kernel Generation

Introduction to Computer Vision

ACT: Accelerator Compiler Toolkit

pytest-gpu-proof: A Simple Pytest Extension for Local GPU Code Testing

Accelerating the Mitigation of LLM Inference Nondeterminism Across GPU Architectures

AutoTuneBench: Trustworthy Measurement for Agent Auto-Tuning of LLM Serving Engines

nvsass-disassembler

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