Programming language support for adaptable wearable computing

2003 ◽  
Author(s):  
P.K. McKinley ◽  
S.M. Sadjadi ◽  
E.P. Kasten ◽  
R. Kalaskar
Keyword(s):  
Programming Language ◽  
Wearable Computing ◽  
Language Support

scholarly journals 22. Programming language support for supercomputers*

1986 ◽  
pp. 282-311
Author(s):  
R. T. Hood ◽  
K. Kennedy
Keyword(s):  
Programming Language ◽  
Language Support

Ferret: Programming language support for multiple dynamic classification

2009 ◽  
Vol 35 (3) ◽  
pp. 306-321 ◽  
Author(s):  
Bard Bloom ◽  
Paul Keyser ◽  
Ian Simmonds ◽  
Mark Wegman
Keyword(s):  
Programming Language ◽  
Language Support

Programming language support for analyzing non-persistent data

2016 ◽  
Author(s):  
Yung-Hsiang Lu ◽  
Milind Kulkarni ◽  
Nouraldin Jaber ◽  
Jerry Xiaojin Zhu
Keyword(s):  
Programming Language ◽  
Language Support ◽  
Persistent Data

Language support for parallel and distributed computing

2021 ◽  
Vol 40 (2) ◽  
pp. 51-54
Author(s):  
Kyle Chard ◽  
James Muns ◽  
Richard Wai ◽  
S. Tucker Taft
Keyword(s):  
Parallel Computing ◽  
Distributed Computing ◽  
Programming Language ◽  
Divide And Conquer ◽  
Parallel And Distributed Computing ◽  
Communication Costs ◽  
Language Support ◽  
World Communication ◽  
Language Constructs ◽  
Parallel Loops

Language constructs that support parallel computing are relatively well recognized at this point, with features such as parallel loops (optionally with reduction operators), divide-and-conquer parallelism, and general parallel blocks. But what language features would make distributed computing safer and more productive? Is it helpful to be able to specify on what node a computation should take place, and on what node data should reside, or is that overspecification? We don't normally expect a user of a parallel programming language to specify what core is used for a given iteration of a loop, nor which data should be moved into which core's cache. Generally the compiler and the run-time manage the allocation of cores, and the hardware worries about the cache. But in a distributed world, communication costs can easily outweigh computation costs in a poorly designed application. This panel will discuss various language features, some of which already exist to support parallel computing, and how they could be enhanced or generalized to support distributed computing safely and efficiently.

scholarly journals Developing an LLVM-based compiler for stack based TF16 processor architecture

2021 ◽  
Vol 33 (5) ◽  
pp. 137-154
Author(s):  
Leonid Vladlenovich Skvortsov ◽  
Roman Vyacheslavovich Baev ◽  
Ksenia Yurievna Dolgorukova ◽  
Eugene Yurievich Sharygin
Keyword(s):  
Programming Language ◽  
Development Process ◽  
Scheduling Algorithm ◽  
Original Version ◽  
Processor Architecture ◽  
Assembly Code ◽  
Language Support ◽  
Low Level ◽  
C Compiler ◽  
High Level

Development for stack-based architectures is usually done using legacy low level languages or assembly code, so there exists a problem of a high level programming language support for such architectures. In this paper we describe the development process of an LLVM/Clang-based C compiler for stack-based TF16 processor architecture. LLVM was used due to adaptation possibilities of its components for new architectures, such as disassembler, linker and debugger. Two compiler versions were developed. The first version generated code without using stack capabilities of TF16, treating it instead as a register-based architecture. This version was relatively easy to develop and it provided us a comparison point for the second one. In the second version we have implemented a platform independent stack scheduling algorithm that allowed us to generate code that makes use of the stack capabilities of the CPU. When comparing the two versions, a version that utilized stack capabilities generated code that was on average 35.7% faster and 50.8% smaller than the original version. The developed stack scheduling algorithm also allows to support other stack based architectures in LLVM toolchain.

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