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In computing, an optimizing compiler is a compiler that tries to minimize or maximize some attributes of an executable computer program. Common requirements are to minimize a program's execution time, memory footprint, storage size, and power consumption.
In computer science, program optimization, code optimization, or software optimization is the process of modifying a software system to make some aspect of it work more efficiently or use fewer resources. In general, a computer program may be optimized so that it executes more rapidly, or to make it capable of operating with less memory storage or other resources, or draw less power.
In compiler optimization, register allocation is the process of assigning local automatic variables and expression results to a limited number of processor registers.
Compute Unified Device Architecture (CUDA) is a proprietary parallel computing platform and application programming interface (API) that allows software to use certain types of graphics processing units (GPUs) for accelerated general-purpose processing, an approach called general-purpose computing on GPUs (GPGPU). CUDA API and its runtime: The CUDA API is an extension of the C programming language that adds the ability to specify thread-level parallelism in C and also to specify GPU device specific operations (like moving data between the CPU and the GPU). CUDA is a software layer that gives direct access to the GPU's virtual instruction set and parallel computational elements for the execution of compute kernels. In addition to drivers and runtime kernels, the CUDA platform includes compilers, libraries and developer tools to help programmers accelerate their applications.
The Collective Tuning Initiative is a community-driven initiative started by Grigori Fursin to develop free and open-source research tools with a unified API for collaborative characterization, optimization and co-design of computer systems. They enable sharing of benchmarks, data sets and optimization cases from the community in the Collective Optimization Database through unified web services to predict better optimizations or architecture designs. Using common research-and-development tools should help to improve the quality and reproducibility of computer systems' research and development and accelerate innovation in this area. This approach helped establish Reproducibility Initiatives and Artifact Evaluation at several ACM-sponsored conferences to encourage sharing of artifacts and validation of experimental results from accepted papers.
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TensorFlow is a free and open-source software library for machine learning and artificial intelligence. It can be used across a range of tasks but has a particular focus on training and inference of deep neural networks.
ILNumerics is a mathematical class library for Common Language Infrastructure (CLI) developers and a domain specific language (DSL) for the implementation of numerical algorithms on the .NET platform. While algebra systems with graphical user interfaces focus on prototyping of algorithms, implementation of such algorithms into distribution-ready applications is done using development environments and general purpose programming languages (GPL). ILNumerics is an extension to Visual Studio and aims at supporting the creation of technical applications based on .NET.
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Tensor Processing Unit (TPU) is an AI accelerator application-specific integrated circuit (ASIC) developed by Google for neural network machine learning, using Google's own TensorFlow software. Google began using TPUs internally in 2015, and in 2018 made them available for third-party use, both as part of its cloud infrastructure and by offering a smaller version of the chip for sale.
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Owl Scientific Computing is a software system for scientific and engineering computing developed in the Department of Computer Science and Technology, University of Cambridge. The System Research Group (SRG) in the department recognises Owl as one of the representative systems developed in SRG in the 2010s. The source code is licensed under the MIT License and can be accessed from the GitHub repository.
Google JAX is a machine learning framework for transforming numerical functions, to be used in Python. It is described as bringing together a modified version of autograd and TensorFlow's XLA. It is designed to follow the structure and workflow of NumPy as closely as possible and works with various existing frameworks such as TensorFlow and PyTorch. The primary functions of JAX are:
- grad: automatic differentiation
- jit: compilation
- vmap: auto-vectorization
- pmap: SPMD programming
Tensor informally refers in machine learning to two different concepts that organize and represent data. Data may be organized in a multidimensional array (M-way array) that is informally referred to as a "data tensor"; however in the strict mathematical sense, a tensor is a multilinear mapping over a set of domain vector spaces to a range vector space. Observations, such as images, movies, volumes, sounds, and relationships among words and concepts, stored in an M-way array ("data tensor") may be analyzed either by artificial neural networks or tensor methods.
References
- ↑ Hampton, Jaime (2022-10-12). "Google Announces Open Source ML Compiler Project, OpenXLA". EnterpriseAI. Archived from the original on 2023-12-10. Retrieved 2023-12-10.
- ↑ Woodie, Alex (2023-03-09). "OpenXLA Delivers Flexibility for ML Apps". Datanami. Retrieved 2023-12-10.
- ↑ "TensorFlow XLA: Accelerated Linear Algebra". TensorFlow Official Documentation. Retrieved 2023-12-10.
- ↑ Smith, John (2022-07-15). "Optimizing TensorFlow Models with XLA". Journal of Machine Learning Research. 23: 45–60.
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