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Explorations of the Viability of ARM and Xeon Phi for Physics Processing

David Abdurachmanov, Kapil Arya, Josh Bendavid, Tommaso Boccali, Gene Cooperman, Andrea Dotti, Peter Elmer, Giulio Eulisse, Francesco Giacomini, Christopher D. Jones, Matteo Manzali, Shahzad Muzaffar

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Tags: ARM, Computational Physics, High Energy Physics - Experiment, Intel Phi, Intel Xeon Phi, Performance, Physics

November 11, 2013 by hgpu

Towards a Portable and Future-proof Particle-in-Cell Plasma Physics Code

R. F. Bird, S. J. Pennycook, S. A. Wright, S. A. Jarvis

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Tags: Algorithms, Computer science, nVidia, OpenCL, Particle-in-cell methods, Physics, Plasma physics, Tesla C1060, Tesla C2050, Tesla K20

November 10, 2013 by hgpu

Many-core applications to online track reconstruction in HEP experiments

S. Amerio, D. Bastieri, M. Corvo, A. Gianelle, W. Ketchum, T. Liu, A. Lonardo, D. Lucchesi, S. Poprocki, R. Rivera, L. Tosoratto, P. Vicini, P. Wittich

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Tags: CUDA, High Energy Physics - Experiment, Intel Phi, Intel Xeon Phi, nVidia, nVidia GeForce GTX Titan, Physics, Tesla K20, Tesla M2050

November 6, 2013 by hgpu

NaNet:a low-latency NIC enabling GPU-based, real-time low level trigger systems

Roberto Ammendola, Andrea Biagioni, Riccardo Fantechi, Ottorino Frezza, Gianluca Lamanna, Francesca Lo Cicero, Alessandro Lonardo, Pier Stanislao Paolucci, Felice Pantaleo, Roberto Piandani, Luca Pontisso, Davide Rossetti, Francesco Simula, Marco Sozzi, Laura Tosoratto, Piero Vicini

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Tags: CUDA, FPGA, nVidia, Physics, Tesla K20, Tesla M2070

November 6, 2013 by hgpu

A new GPU-accelerated hydrodynamical code for numerical simulation of interacting galaxies

Igor Kulikov

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Tags: Astrophysics, CUDA, Gravitation, Instrumentation and Methods for Astrophysics, Laplace and Poisson equation, MPI, Numerical simulation, nVidia, Poisson equation

November 6, 2013 by hgpu

Performance of Kepler GTX Titan GPUs and Xeon Phi System

Hwancheol Jeong, Weonjong Lee, Jeonghwan Pak, Kwang-jong Choi, Sang-Hyun Park, Jun-sik Yoo, Joo Hwan Kim, Joungjin Lee, Young Woo Lee

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Tags: Computational Physics, CUDA, Intel Phi, Intel Xeon Phi, nVidia, nVidia GeForce GTX Titan, Performance, Physics, Tesla K20

November 6, 2013 by hgpu

Use of Checkpoint-Restart for Complex HEP Software on Traditional Architectures and Intel MIC

Kapil Arya, Gene Cooperman, Andrea Dotti, Peter Elmer

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Tags: Computational Physics, High Energy Physics - Experiment, Intel Phi, Intel Xeon Phi, Numerical Analysis, Physics

November 4, 2013 by hgpu

Initial Explorations of ARM Processors for Scientific Computing

David Abdurachmanov, Peter Elmer, Giulio Eulisse, Shahzad Muzaffar

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Tags: ARM, Computational Physics, Energy-efficient computing, High Energy Physics - Experiment, Numerical Analysis, Physics

November 4, 2013 by hgpu

Accelerated rescaling of single Monte Carlo simulation runs with the Graphics Processing Unit (GPU)

Owen Yang, Bernard Choi

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Tags: CUDA, Monte Carlo simulation, nVidia, nVidia GeForce GT 650 M, Optics, Package, Physics

November 3, 2013 by hgpu

N-Body Simulation Using GP-GPU: Evaluating Host/Device Memory Transference Overhead

Sergio M. Martin, Fernando G. Tinetti, Nicanor B. Casas, Graciela E. De Luca, Daniel A. Giulianelli

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Tags: Astrophysics, CUDA, Gravitation, N-body simulation, nVidia, nVidia GeForce GTX 550 Ti, Physics

November 3, 2013 by hgpu

The Plasma Simulation Code: A modern particle-in-cell code with load-balancing and GPU support

Kai Germaschewski, William Fox, Narges Ahmadi, Liang Wang, Stephen Abbott, Hartmut Ruhl, Amitava Bhattacharjee

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Tags: Computational Physics, CUDA, nVidia, Particle-in-cell methods, Physics, Plasma physics, Tesla C1060, Tesla K20

October 30, 2013 by hgpu

First Evaluation of the CPU, GPGPU and MIC Architectures for Real Time Particle Tracking based on Hough Transform at the LHC

V. Halyo, P. LeGresley, P. Lujan, V. Karpusenko, A. Vladimirov

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Tags: Algorithms, Computational Physics, CUDA, High Energy Physics - Experiment, Intel Phi, Intel Xeon Phi, nVidia, Physics, Tesla K20

October 29, 2013 by hgpu

PELSI: Power-Efficient Layer-Switched Inference

Efficient deep learning inference on end devices

Ouroboros: Virtualized Queues for dynamic memory management

Dynamic Memory Management on GPUs with SYCL

MSCCL++: A GPU-driven communication stack for scalable AI applications

MSCCL++: Rethinking GPU Communication Abstractions for Cutting-edge AI Applications

Benchmark compute shader of Unity against InteropUnityCUDA

InteropUnityCUDA: A Tool for Interoperability Between Unity and CUDA

Data-efficient LLM Fine-tuning for Code Generation

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

DeepCompile: A Compiler-Driven Approach to Optimizing Distributed Deep Learning Training

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

GigaAPI: a user-space API that simplifies multi-GPU programming, bridging the gap between the capabilities of parallel GPU systems and the ability of developers to harness their full potential

GigaAPI for GPU Parallelization

Coccinelle: a C code transformation engine using SmPL for matches, refactorings, and bug fixing

Advances in Semantic Patching for HPC-oriented Refactorings with Coccinelle

DuoReduce: MLIR's benchmark

Hardware-Assisted Software Testing and Debugging for Heterogeneous Computing

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