Region-based memory management for GPU programming languages

2014 ◽  
Vol 49 (10) ◽  
pp. 141-155 ◽  
Author(s):  
Eric Holk ◽  
Ryan Newton ◽  
Jeremy Siek ◽  
Andrew Lumsdaine
Keyword(s):  
Programming Languages ◽  
Memory Management ◽  
Gpu Programming

scholarly journals EVector: An Efficient Vector Implementation – Using Virtual Memory for Improving Memory

2006 ◽  
Vol 14 (2) ◽  
pp. 45-59
Author(s):  
Dries Kimpe ◽  
Stefan Vandewalle ◽  
Stefaan Poedts
Keyword(s):  
Operating System ◽  
Programming Languages ◽  
Operating Systems ◽  
Memory Management ◽  
Object Oriented ◽  
Virtual Memory ◽  
Management Unit ◽  
Efficient Vector ◽  
Reference Implementation

Every modern operating system provides some form of virtual memory to its applications. Usually, a hardware memory management unit (MMU) exists to efficiently support this. Although most operating systems allow user programs to indirectly control the MMU, few programs or programming languages actually make use of this facility. This article explores how the MMU can be used to enhance memory handling for resizable arrays. A reference implementation in C++ demonstrates its usability and superiority compared to the standard C++ vector class, and how to combine the scheme with an object-oriented environment. A number of other improvements, based on newly emerged insights in C++ are also presented.

scholarly journals Comparing Ease of Programming in C++, Go, and Java for Implementing a Next-Generation Sequencing Tool

2019 ◽  
Vol 15 ◽  
pp. 117693431986901
Author(s):  
Pascal Costanza ◽  
Charlotte Herzeel ◽  
Wilfried Verachtert
Keyword(s):  
Next Generation Sequencing ◽  
Programming Languages ◽  
Memory Management ◽  
Software Framework ◽  
Next Generation ◽  
Computational Performance ◽  
Library Support ◽  
Reasonable Choice ◽  
Generation Sequencing ◽  
Standard Library

elPrep is an extensible multithreaded software framework for efficiently processing Sequence Alignment/Map (SAM)/Binary Alignment/Map (BAM) files in next-generation sequencing pipelines. Similar to other SAM/BAM tools, a key challenge in elPrep is memory management, as such programs need to manipulate large amounts of data. We therefore investigated 3 programming languages with support for assisted or automated memory management for implementing elPrep, namely C++, Go, and Java. We implemented a nontrivial subset of elPrep in all 3 programming languages and compared them by benchmarking their runtime performance and memory use to determine the best language in terms of computational performance. In a previous article, we motivated why, based on these results, we eventually selected Go as our implementation language. In this article, we discuss the difficulty of achieving the best performance in each language in terms of programming language constructs and standard library support. While benchmarks are easy to objectively measure and evaluate, this is less obvious for assessing ease of programming. However, because we expect elPrep to be regularly modified and extended, this is an equally important aspect. We illustrate representative examples of challenges in all 3 languages, and give our opinion why we think that Go is a reasonable choice also in this light.

scholarly journals A performance comparison of garbage collector algorithms in Java Virtual Machine

2019 ◽  
Vol 13 ◽  
pp. 359-365
Author(s):  
Igor Kopeć ◽  
Jakub Smołka
Keyword(s):  
Programming Languages ◽  
Memory Management ◽  
Garbage Collection ◽  
Performance Comparison ◽  
Java Virtual Machine ◽  
Garbage Collector ◽  
System Responsiveness ◽  
Automatic Memory ◽  
A Performance

In programming languages with automatic memory management garbage collection plays an important role of cleaning unused memory. Garbage collection algorithms have been developed for many years and aim to maximize the application’s performance. This paper presents and compares a performance of five garbage collection algorithms present in current version of Java 12 in three applications with different object lifetime span. The analysis covered the system responsiveness, garbage collector workload and application throughput at high application load.

scholarly journals Comparative Analysis of C++ and Python in Terms of Memory and Time

2020 ◽  
Author(s):  
Farzeen Zehra ◽  
Maha Javed ◽  
Darakhshan Khan ◽  
Maria Pasha
Keyword(s):  
Comparative Analysis ◽  
Programming Languages ◽  
Programming Language ◽  
Operating Systems ◽  
Memory Management ◽  
Object Oriented ◽  
Program Execution ◽  
Execution Speed ◽  
Management Techniques ◽  
High Level

In this era of technology, programming has become more significant than ever before. Python and C++ are both widely used programming languages. Python, the most popular programming language in today’s world, is a high-level object-oriented language whereas C++, the language behind most operating systems, is a low-level object-oriented language. In this paper, we present a comparative study of Python and C++. This paper discusses the introduction to these languages, their memory management techniques, and the reasons behind their program execution speed. Furthermore, we analyzed the execution time and memory used by multiple algorithms in both the languages with best, average, and worst cases. They are also compared with respect to the benefits and issues related to them. Results indicate that C++ is faster than Python in execution speed but Python serves as a better language for beginners due to its simplicity. Moreover, for the best results, the language should be selected according to the type of project.

Other GPU Programming Languages

2018 ◽  
pp. 413-423
Author(s):  
Sam Miller ◽  
Andrew Boggio-Dandry ◽  
Tolga Soyata
Keyword(s):  
Programming Languages ◽  
Gpu Programming

scholarly journals Distributed communication in ML

2000 ◽  
Vol 10 (1) ◽  
pp. 91-120
Author(s):  
MARK HAYDEN
Keyword(s):  
Programming Languages ◽  
Memory Management ◽  
Group Communication ◽  
Careful Attention ◽  
Distributed Communication ◽  
High Level ◽  
Automatic Memory ◽  
High Level Abstraction

We present our experience in implementing a group communication toolkit in Objective Caml, a dialect of the ML family of programming languages. We compare the toolkit both quantitatively and qualitatively to a predecessor toolkit which was implemented in C. Our experience shows that using the high-level abstraction features of ML gives substantial advantages. Some of these features, such as automatic memory management and message marshalling, allowed us to concentrate on those pieces of the implementation which required careful attention in order to achieve good performance. We conclude with a set of suggested changes to ML implementations.

Sign in / Sign up

Export Citation Format

Share Document