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Parallel Acceleration on Manycore Systems and Its Performance Analysis: OpenCL Case Study

Rafael Alejandro Vejarano, Phuong Thi Yen, Jeong-Gun Lee

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Tags: Computer science, Heterogeneous systems, Matrix multiplication, nVidia, nVidia GeForce GTX 460, OpenCL, Performance

June 4, 2013 by hgpu

Plasma Visualization in Parallel using Particle Systems on Graphical Processing Units

Timothy S. Lyes, K. A. Hawick

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Tags: Computer science, CUDA, Electrostatics, nVidia, nVidia GeForce GTX 680, nVidia Quadro FX 4000, Plasma physics, Rendering, Visualization

June 4, 2013 by hgpu

Using sparse optical flow for multiple Kinect applications

Kai Berger, Yannic Schroeder, Stefan Guthe, Marc Aurel Kastner

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Tags: Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GTX 690, Optical flow

June 4, 2013 by hgpu

Data-driven versus Topology-driven Irregular Computations on GPUs

Rupesh Nasre, Martin Burtscher, Keshav Pingali

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia Quadro FX 6000

June 4, 2013 by hgpu

GPU Acceleration of a Genetic Algorithm for the Synthesis of FSM-based Bimodal Predictors

Martin Burtscher, Hassan Rabeti

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Tags: Algorithms, CAD, Computer science, CUDA, Genetic programming, nVidia, nVidia GeForce GTX 680

June 4, 2013 by hgpu

CUDA Leaks: Information Leakage in GPU Architectures

Roberto Di Pietro, Flavio Lombardi, Antonio Villani

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GT 640, Security, Tesla C2050

June 4, 2013 by hgpu

GPU-accelerated generation of correctly-rounded elementary functions

Pierre Fortin, Mourad Gouicem, Stef Graillat

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Tags: Algorithms, Computer science, CUDA, Elementary functions, nVidia, Tesla C2070

June 2, 2013 by hgpu

GPUburn: A System to Test and Mitigate GPU Hardware Failures

David Defour, Eric Petit

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Tags: Benchmarking, Computer science, CUDA, Fault tolerance, nVidia, OpenCL, Tesla D870

June 2, 2013 by hgpu

Novel Multi-Layer Network Decomposition Boosting Acceleration of Multi-core Algorithms

Athanasios K. Grivas, Terrence Maky, Alex Yakovlev, Jonny Wrayz

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 560

June 2, 2013 by hgpu

On GPU Fourier Transformations

Giso Dal

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Tags: Algorithms, Computer science, CUDA, FFT, nVidia, nVidia GeForce GTX 480, Thesis

June 2, 2013 by hgpu

Task scheduling in hybrid CPU-GPU systems

Martin Krulis, Zbynek Falt, David Bednarek, Jakub Yaghob

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Tags: Computer science, Heterogeneous systems, nVidia, nVidia GeForce GTX 580, OpenCL, Operating systems, Task scheduling, Tesla M2090

June 2, 2013 by hgpu

Composition and Reuse with Compiled Domain-Specific Languages

Arvind K. Sujeeth, Tiark Rompf, Kevin J. Brown, HyoukJoong Lee, Hassan Chafi, Victoria Popic, Michael Wu, Aleksander Prokopec, Vojin Jovanovic, Martin Odersky, Kunle Olukotun

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Tags: Computer science, CUDA, MPI, nVidia, Package, Performance, Tesla C2050

May 31, 2013 by hgpu

OpScanner

A Study of Floating-Point Precision Tuning in Deep Learning Operators Implementations

Atlas CLI: Machine Learning (ML) Lifecycle & Transparency Manager

Scalable GPU-Based Integrity Verification for Large Machine Learning Models

transformers_tvm: Implementation of Encoder Decoder transformer on TVM

Enhancing Transformer Performance and Portability through Auto-tuning Frameworks

INT v.s. FP: A framework to compare low-bit integer and float-point formats

INT v.s. FP: A Comprehensive Study of Fine-Grained Low-bit Quantization Formats

AutoDock-GPU: AutoDock for GPUs and other accelerators

Architecting Tensor Core-Based Reductions for Irregular Molecular Docking Kernels

NCCLX: collective communication framework

Collective Communication for 100k+ GPUs

Tutoring LLM into a Better CUDA Optimizer

Tutoring LLM into a Better CUDA Optimizer

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Kernel Library for LLM Serving

Compiler and Runtime Systems for Generative AI Models

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

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