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Exploring Different Automata Representations for Efficient Regular Expression Matching on GPUs

Xiaodong Yu, Michela Becchi

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Tags: Cellular automata, Computer science, CUDA, nVidia, Performance, Security

January 28, 2013 by hgpu

Warped Register File: A Power Efficient Register File for GPGPUs

Mohammad Abdel-Majeed, Murali Annavaram

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Tags: Computer science, CUDA, GPGPU-sim, Hardware Architecture, nVidia, Performance

January 28, 2013 by hgpu

GPUfs: Integrating a File System with GPUs

Mark Silberstein, Bryan Ford, Idit Keidar, Emmett Witchel

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Tags: Computer science, CUDA, nVidia, Operating systems, Tesla C2075

January 26, 2013 by hgpu

Advanced Trends of Heterogeneous Computing with CPU-GPU Integration: Comparative Study

Ishan Rajani, G Nanda Gopal

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Tags: AMD FireStream 9270, ATI, Computer science, CUDA, Distributed computing, Heterogeneous systems, nVidia, OpenCL, Tesla C2050, Warp level parallelism

January 26, 2013 by hgpu

Autotuning, Code Generation and Optimizing Compiler Technology for GPUs

Malik Muhammad Zaki Murtaza Khan

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Tags: Code generation, Computer science, CUBLAS, CUDA, Linear Algebra, nVidia, Tesla C2050, Thesis

January 26, 2013 by hgpu

Automatic Parallelization for GPUs

Thomas B. Jablin

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 480, Programming techniques, Thesis

January 26, 2013 by hgpu

Orthogonalization on a General Purpose Graphics Processing Unit with Double Double and Quad Double Arithmetic

Jan Verschelde, Genady Yoffe

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Tags: Algorithms, CUDA, Extended precision, Mathematics, nVidia, Tesla C2050

January 25, 2013 by hgpu

Regularization and nonlinearities for neural language models: when are they needed?

Marius Pachitariu, Maneesh Sahani

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Tags: CUDA, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 680, Statistics

January 25, 2013 by hgpu

Locality-Aware Work Stealing on Multi-CPU and Multi-GPU Architectures

Thierry Gautier, Joao V. F. Lima, Nicolas Maillard, Bruno Raffin

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Tags: Computer science, CUDA, Factorization, Heterogeneous systems, Linear Algebra, nVidia, Package, Tesla C2050

January 25, 2013 by hgpu

A GPU-accelerated Direct-sum Boundary Integral Poisson-Boltzmann Solver

Weihua Geng, Ferosh Jacob

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Tags: Biomolecules, CUDA, Electrostatics, Linear Algebra, Mathematics, Molecular surface, Numerical Analysis, nVidia, Poisson Boltzmann, Tesla M2070

January 25, 2013 by hgpu

High Performance Lattice Boltzmann Solvers on Massively Parallel Architectures with Applications to Building Aeraulics

Christian Obrecht

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Tags: Boltzmann equation, CUDA, Fluid dynamics, Lattice Boltzmann model, Navier-Stokes equations, NSEs, Numerical Analysis, nVidia, nVidia GeForce GTX 295, Thesis

January 24, 2013 by hgpu

Programmability and Performance Portability Aspects of Heterogeneous Multi-/Manycore Systems

Christoph Kessler, Usman Dastgeer, Samuel Thibault, Raymond Namyst, Andrew Richards, Uwe Dolinsky, Siegfried Benkner, Jesper Larsson Traff, Sabri Pllana

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Tags: Computer science, CUDA, Heterogeneous systems, MPI, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 480, Performance, Tesla C1060, Tesla C2050

January 24, 2013 by hgpu

Kokkidio: Kokkos+Eigen - write fast, readable multi-platform code

Kokkidio: Fast, expressive, portable code, based on Kokkos and Eigen

LLaMA-Inference-Bench

LLM-Inference-Bench: Inference Benchmarking of Large Language Models on AI Accelerators

MATAR

On a Simplified Approach to Achieve Parallel Performance and Portability Across CPU and GPU Architectures

Over-synchronization in GPU Programs

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

Profile Util library: A quick and easy way to get MPI, OpenMP and GPU runtime information

MaC: Mamba CPU performance predictor

MambaCPU: Enhanced Correlation Mining with State Space Models for CPU Performance Prediction

Mixed-precision finite element kernels

Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration

SHMT: The Simultaneous and Heterogenous Multithreading project

General-Purpose Computing on Tensor Processors

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

Online Energy Optimization in GPUs: A Multi-Armed Bandit Approach

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