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Relevant bibliographies by topics / GPU code generation

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Journal articles

Academic literature on the topic 'GPU code generation'

Author: Grafiati

Published: 6 September 2023

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Journal articles on the topic "GPU code generation"

1

EMMART, NIALL, and CHARLES WEEMS. "SEARCH-BASED AUTOMATIC CODE GENERATION FOR MULTIPRECISION MODULAR EXPONENTIATION ON MULTIPLE GENERATIONS OF GPU." Parallel Processing Letters 23, no. 04 (2013): 1340009. http://dx.doi.org/10.1142/s0129626413400094.

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Multiprecision modular exponentiation has a variety of uses, including cryptography, prime testing and computational number theory. It is also a very costly operation to compute. GPU parallelism can be used to accelerate these computations, but to use the GPU efficiently, a problem must involve many simultaneous exponentiation operations. Handling a large number of TLS/SSL encrypted sessions in a data center is an important problem that fits this profile. We are developing a framework that enables generation of highly efficient implementations of exponentiation operations for different NVIDIA

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2

Afar Nazim, Allazov. "Automatic Generation of GPU Code in DVOR." University News. North-Caucasian Region. Technical Sciences Series, no. 3 (September 2015): 3–9. http://dx.doi.org/10.17213/0321-2653-2015-3-3-9.

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3

Blazewicz, Marek, Ian Hinder, David M. Koppelman, et al. "From Physics Model to Results: An Optimizing Framework for Cross-Architecture Code Generation." Scientific Programming 21, no. 1-2 (2013): 1–16. http://dx.doi.org/10.1155/2013/167841.

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Starting from a high-level problem description in terms of partial differential equations using abstract tensor notation, theChemoraframework discretizes, optimizes, and generates complete high performance codes for a wide range of compute architectures. Chemora extends the capabilities of Cactus, facilitating the usage of large-scale CPU/GPU systems in an efficient manner for complex applications, without low-level code tuning. Chemora achieves parallelism through MPI and multi-threading, combining OpenMP and CUDA. Optimizations include high-level code transformations, efficient loop traversa

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4

Rodrigues, A. Wendell O., Frédéric Guyomarc'h, Jean-Luc Dekeyser, and Yvonnick Le Menach. "Automatic Multi-GPU Code Generation Applied to Simulation of Electrical Machines." IEEE Transactions on Magnetics 48, no. 2 (2012): 831–34. http://dx.doi.org/10.1109/tmag.2011.2179527.

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5

Rawat, Prashant Singh, Miheer Vaidya, Aravind Sukumaran-Rajam, et al. "Domain-Specific Optimization and Generation of High-Performance GPU Code for Stencil Computations." Proceedings of the IEEE 106, no. 11 (2018): 1902–20. http://dx.doi.org/10.1109/jproc.2018.2862896.

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6

Basu, Protonu, Samuel Williams, Brian Van Straalen, Leonid Oliker, Phillip Colella, and Mary Hall. "Compiler-based code generation and autotuning for geometric multigrid on GPU-accelerated supercomputers." Parallel Computing 64 (May 2017): 50–64. http://dx.doi.org/10.1016/j.parco.2017.04.002.

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7

Klöckner, Andreas, Nicolas Pinto, Yunsup Lee, Bryan Catanzaro, Paul Ivanov, and Ahmed Fasih. "PyCUDA and PyOpenCL: A scripting-based approach to GPU run-time code generation." Parallel Computing 38, no. 3 (2012): 157–74. http://dx.doi.org/10.1016/j.parco.2011.09.001.

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8

Hagiescu, Andrei, Bing Liu, R. Ramanathan, et al. "GPU code generation for ODE-based applications with phased shared-data access patterns." ACM Transactions on Architecture and Code Optimization 10, no. 4 (2013): 1–19. http://dx.doi.org/10.1145/2541228.2555311.

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9

Holzer, Markus, Martin Bauer, Harald Köstler, and Ulrich Rüde. "Highly efficient lattice Boltzmann multiphase simulations of immiscible fluids at high-density ratios on CPUs and GPUs through code generation." International Journal of High Performance Computing Applications 35, no. 4 (2021): 413–27. http://dx.doi.org/10.1177/10943420211016525.

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A high-performance implementation of a multiphase lattice Boltzmann method based on the conservative Allen-Cahn model supporting high-density ratios and high Reynolds numbers is presented. Meta-programming techniques are used to generate optimized code for CPUs and GPUs automatically. The coupled model is specified in a high-level symbolic description and optimized through automatic transformations. The memory footprint of the resulting algorithm is reduced through the fusion of compute kernels. A roofline analysis demonstrates the excellent efficiency of the generated code on a single GPU. Th

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10

Walsh, Stuart D. C., and Martin O. Saar. "Developing Extensible Lattice-Boltzmann Simulators for General-Purpose Graphics-Processing Units." Communications in Computational Physics 13, no. 3 (2013): 867–79. http://dx.doi.org/10.4208/cicp.351011.260112s.

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AbstractLattice-Boltzmann methods are versatile numerical modeling techniques capable of reproducing a wide variety of fluid-mechanical behavior. These methods are well suited to parallel implementation, particularly on the single-instruction multiple data (SIMD) parallel processing environments found in computer graphics processing units (GPUs).Although recent programming tools dramatically improve the ease with which GPUbased applications can be written, the programming environment still lacks the flexibility available to more traditional CPU programs. In particular, it may be difficult to d

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