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GPU Accelerated Scalable Parallel Random Number Generators

Shuang Gao, Gregory D. Peterson

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Tags: Computer science, CUDA, FPGA, nVidia, nVidia GeForce GTX 480, Pseudo-random number generators, Tesla S1060

February 19, 2011 by hgpu

High-order finite-element seismic wave propagation modeling with MPI on a large GPU cluster

Dimitri Komatitsch, Gordon Erlebacher, Dominik Goddeke, David Michea

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Tags: Computational Physics, CUDA, Finite element method, Geoscience, GPU cluster, MPI, Numerical simulation, nVidia, Physics, Seismic modeling, Spectral elements, Tesla S1060, Tesla S1070

October 29, 2010 by hgpu

ATTUNE: Agentic Throughput Tuning for LLM Training

ATTUNE: Agentic Throughput Tuning for LLM Training

HPCAgent-Bench: A Framework for Sound Benchmarking of Code-Optimizing Agents, Harnesses, and Skills

HPCAgent-Bench: A Framework for Sound Benchmarking of Code-Optimizing Agents, Harnesses, and Skills

AgentGarten: Code Worlds for Evolving Agents

AgentGarten: Code Worlds for Evolving Agents

Brian2 Atlas

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

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

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

Introduction to Computer Vision

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

Accelerating the Mitigation of LLM Inference Nondeterminism Across GPU Architectures

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