Sparse matrix-vector multiplication (SpMV) is of singular importance in sparse linear algebra. In contrast to the uniform regularity of dense linear algebra, sparse operations encounter a broad spectrum of matrices ranging from the regular to the highly irregular. Harnessing the tremendous potential of throughput-oriented processors for sparse operations requires that we expose substantial fine-grained parallelism and impose sufficient regularity on execution paths and memory access patterns. We explore SpMV methods that are well-suited to throughput-oriented architectures like the GPU and which exploit several common sparsity classes. The techniques we propose are efficient, successfully utilizing large percentages of peak bandwidth. Furthermore, they deliver excellent total throughput, averaging 16 GFLOP/s and 10 GFLOP/s in double precision for structured grid and unstructured mesh matrices, respectively, on a GeForce GTX 285. This is roughly 2.8 times the throughput previously achieved on Cell BE and more than 10 times that of a quad-core Intel Clovertown system.

/pdf/implementing-sparse-matrix-vector-multiplication-on-2m98krk6l7.pdf

Implementing sparse matrix-vector multiplication on throughput-oriented processors

In the past 20 years there has been treftlen-dous progress in developing and analyzing parallel algorithftls. Researchers have developed efficient parallel algorithms to solve most problems for which efficient sequential solutions are known. Although some of these algorithms are efficient only in a theoretical framework, many are quite efficient in practice or have key ideas that have been used in efficient implementations. This research on parallel algorithms has not only improved our general understanding ofpar-allelism but in several cases has led to improvements in sequential algorithms. Unf:ortunately there has been less success in developing good languages f:or prograftlftling parallel algorithftls, particularly languages that are well suited for teaching and pro-totyping algorithms. There has been a large gap between languages that are too low level, requiring specification of many details that obscure the meaning of the algorithm, and languages that are too high level, making the performance implications of various constructs unclear. In sequential computing many standard languages such as C or Pascal do a reasonable J·ob of bridging this gap, but in parallel languages building such a bridge has been significantly more difficult.

/pdf/programming-parallel-algorithms-2vb49yr2mm.pdf

Programming parallel algorithms

Implementation of a portable nested data-parallel language

Building high-performance virtual machines is a complex and expensive undertaking; many popular languages still have low-performance implementations. We describe a new approach to virtual machine (VM) construction that amortizes much of the effort in initial construction by allowing new languages to be implemented with modest additional effort. The approach relies on abstract syntax tree (AST) interpretation where a node can rewrite itself to a more specialized or more general node, together with an optimizing compiler that exploits the structure of the interpreter. The compiler uses speculative assumptions and deoptimization in order to produce efficient machine code. Our initial experience suggests that high performance is attainable while preserving a modular and layered architecture, and that new high-performance language implementations can be obtained by writing little more than a stylized interpreter.

/pdf/one-vm-to-rule-them-all-2lvuhbbcre.pdf

One VM to rule them all

Traditional techniques for implementing polymorphism use a universal representation for objects of unknown type. Often, this forces a compiler to use universal representations even if the types of objects are known. We examine an alternative approach for compiling polymorphism where types are passed as arguments to polymorphic routines in order to determine the representation of an object. This approach allows monomorphic code to use natural, efficient representations, supports separate compilation of polymorphic definitions and, unlike coercion-based implementations of polymorphism, natural representations can be used for mutable objects such as refs and arrays.We are particularly interested in the typing properties of an intermediate language that allows run-time type analysis to be coded within the language. This allows us to compile many representation transformations and many language features without adding new primitive operations to the language. In this paper, we provide a core target language where type-analysis operators can be coded within the language and the types of such operators can be accurately tracked. The target language is powerful enough to code a variety of useful features, yet type checking remains decidable. We show how to translate an ML-like language into the target language so that primitive operators can analyze types to produce efficient representations. We demonstrate the power of the “user-level” operators by coding flattened tuples, marshalling, type classes, and a form of type dynamic within the language.

/pdf/compiling-polymorphism-using-intensional-type-analysis-2uoccpks24.pdf

Compiling polymorphism using intensional type analysis

The authors outline, compare, and contrast the collections and operations found in many collection-oriented languages by putting them into a common framework. In the process, many problems that can occur in specifying such languages are elucidated. These languages are ideal for use with massively parallel machines, even though many of them were developed before parallelism. Some extended examples of collection operations in several languages are given. A taxonomy of collections is introduced. Issues examined include the type of elements a collection can contain, whether a collection must be homogeneously typed, and the ordering among the elements of a collection. The apply-to-each form in collection-oriented languages is examined. This form applies a function to each element of a collection. Issues treated include whether the extension of a function over the elements is explicit or implicit and how the extension is applied to functions with multiple arguments. A variety of languages (including APL, SETL, CM-Lisp, Paralation Lisp, and Fortran 90) are critically compared. >

/pdf/collection-oriented-languages-3z44bu1y6o.pdf

Collection-oriented languages

: We present our experiences in using Java as an intermediate language for the high level programming language NESL. First, we describe the design and implementation of a system for translating VOODE the current intermediate language used by NESL-into Java. Second, we evaluate this translation by comparing the performance of the original VOODE implementation with several variants of the Java implementation. The translator was easy to build, and the generated Java code achieves reasonable performance when using a just-in-time compiler. We conclude that Java is attractive both as a compilation target for rapid prototyping of new programming languages and as a means of improving the portability of existing programming languages.

Java as an Intermediate Language

CVL is a library of low-level vector routines callable from C. This library includes a wide variety of vector operations such as elementwise function applications, scans, reduces and permutations. Most CVL routines are defined for segmented and unsegmented vectors. This paper is intended for CVL users and implementors, and assumes familiarity with vector operations and the scan-vector model of parallel computation.

http://www.dtic.mil/dtic/tr/fulltext/u2/a270614.pdf

CVL: A C Vector Library Manual: Version 2

: This manual is a supplement to the language definition of NEsL version 3.1. It describes how to use the NEsL system interactively and covers features for accessing on-line help, debugging, profiling, executing programs on remote machines, using NEsL with GNU Emacs, and installing and customizing the NEsL system.

http://www.dtic.mil/cgi-bin/GetTRDoc?AD=ADA302160&Location=U2&doc=GetTRDoc.pdf

NESL User's Manual (for NESL Version 3.1).

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Jay Sipelstein

Author Tools

Chat about Author