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Spartan Daily, December 11, 1972
Volume 60, Issue 51https://scholarworks.sjsu.edu/spartandaily/5686/thumbnail.jp
Tiramisu: A Polyhedral Compiler for Expressing Fast and Portable Code
This paper introduces Tiramisu, a polyhedral framework designed to generate
high performance code for multiple platforms including multicores, GPUs, and
distributed machines. Tiramisu introduces a scheduling language with novel
extensions to explicitly manage the complexities that arise when targeting
these systems. The framework is designed for the areas of image processing,
stencils, linear algebra and deep learning. Tiramisu has two main features: it
relies on a flexible representation based on the polyhedral model and it has a
rich scheduling language allowing fine-grained control of optimizations.
Tiramisu uses a four-level intermediate representation that allows full
separation between the algorithms, loop transformations, data layouts, and
communication. This separation simplifies targeting multiple hardware
architectures with the same algorithm. We evaluate Tiramisu by writing a set of
image processing, deep learning, and linear algebra benchmarks and compare them
with state-of-the-art compilers and hand-tuned libraries. We show that Tiramisu
matches or outperforms existing compilers and libraries on different hardware
architectures, including multicore CPUs, GPUs, and distributed machines.Comment: arXiv admin note: substantial text overlap with arXiv:1803.0041
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Off-line computing for experimental high-energy physics
The needs of experimental high-energy physics for large-scale computing and data handling are explained in terms of the complexity of individual collisions and the need for high statistics to study quantum mechanical processes. The prevalence of university-dominated collaborations adds a requirement for high-performance wide-area networks. The data handling and computational needs of the different types of large experiment, now running or under construction, are evaluated. Software for experimental high-energy physics is reviewed briefly with particular attention to the success of packages written within the discipline. It is argued that workstations and graphics are important in ensuring that analysis codes are correct, and the worldwide networks which support the involvement of remote physicists are described. Computing and data handling are reviewed showing how workstations and RISC processors are rising in importance but have not supplanted traditional mainframe processing. Examples of computing systems constructed within high-energy physics are examined and evaluated
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