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GamePipe: A Virtualized Cloud Platform Design and Performance Evaluation

Zhou Zhao, Kai Hwang, Jose Villeta

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Tags: Cloud, Computer science, CUDA, Games, nVidia, nVidia GeForce 8400 M, Tesla M2070

February 21, 2013 by hgpu

Password Cracking in the Cloud

Gautam Korlam

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Tags: Cloud, Computer science, CUDA, nVidia, nVidia GeForce GTX 260, Security

December 23, 2012 by hgpu

Employing GPU Accelerators for Efficient Enforcement of Data Integrity in Outsourced Data

Krishna Prasanth R, R. Mukkamala, P. K. Baruah

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Tags: Cloud, Computer science, CUDA, nVidia, Security, Tesla M2070, Tesla S2050

December 23, 2012 by hgpu

ValuePack: value-based scheduling framework for CPU-GPU clusters

Vignesh T. Ravi, Michela Becchi, Gagan Agrawal, Srimat Chakradhar

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Tags: Cloud, Computer science, CUDA, GPU cluster, Heterogeneous systems, nVidia, Tesla C2050

November 21, 2012 by hgpu

A Framework for Automated Generation of Specialized Function Variants

Nicholas Chaimov

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Tags: Cloud, Computer science, CUDA, Databases, nVidia, nVidia GeForce GTX 480, Optimization, Tesla C2070, Tesla S1070, Thesis

November 6, 2012 by hgpu

MR-API: A Comprehensive API Framework for Heterogeneous Multi-core Systems using Map Reduce Programming Model

Prashanth Thinakaran, Srinivas Avireddy, Prasanna Ranganathan, Sumalatha Ramachandran

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Tags: Benchmarking, Cloud, Computer science, Heterogeneous systems, MapReduce, nVidia, nVidia GeForce GTX 275

October 22, 2012 by hgpu

Generalized Resource Allocation for the Cloud

Anshul Rai, Ranjita Bhagwan, Saikat Guha

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Tags: ATI, ATI Radeon HD 6990, Cloud, Computer science, nVidia, OpenCL, Performance, Tesla C2075, Virtualization

October 17, 2012 by hgpu

Case Studies in Acceleration of Heston’s Stochastic Volatility Financial Engineering Model: GPU, Cloud and FPGA Implementations

Christos Delivorias

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Tags: Cloud, CUDA, Finance, FPGA, nVidia, Tesla K10, Thesis

August 30, 2012 by hgpu

A virtual memory based runtime to support multi-tenancy in clusters with GPUs

Michela Becchi, Kittisak Sajjapongse, Ian Graves, Adam Procter, Vignesh Ravi, Srimat Chakradhar

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Tags: Cloud, Computer science, CUDA, GPU cluster, Heterogeneous systems, nVidia, PC cluster, Tesla C1060, Tesla C2050, Virtualization

July 27, 2012 by hgpu

Runtime Systems and Scheduling Support for High-End CPU-GPU Architectures

Vignesh Trichy Ravi

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Tags: Cloud, Computer science, CUDA, GPU cluster, Heterogeneous systems, nVidia, OpenCL, Tesla C2050, Thesis

July 10, 2012 by hgpu

Hadoop+Aparapi: Making heterogenous MapReduce programming easier

Yu Lin, Semih Okur, Cosmin Radoi

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Tags: Algorithms, Cloud, Computer science, Java, MapReduce, OpenCL

April 10, 2012 by hgpu

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Tilus: A Tile-Level GPGPU Programming Language for Low-Precision Computation

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

BoltzGen:Toward Universal Binder Design

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

Accurate Models of NVIDIA Tensor Cores

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

TritonForge: Profiling-Guided Framework for Automated Triton Kernel Optimization

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

tritonBLAS: Triton-based Analytical Approach for GEMM Kernel Parameter Selection

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