World's Best Scientists 2026 revealed!
Saman Amarasinghe

Saman Amarasinghe

D-Index & Metrics

Computer Science

D-Index
78
Citations
26484
World Ranking
1198
National Ranking
635

Research.com Recognitions

  • 2019 - ACM Fellow For contributions to high performance computing on modern hardware platforms, domain-specific languages, and compilation techniques

Overview

Saman Amarasinghe is affiliated with the Massachusetts Institute of Technology (MIT) in the United States. Their primary research focus lies within computer science, with significant contributions in hardware and architecture, artificial intelligence, computer networks and communications, computational mathematics, and computer vision and pattern recognition.

Their research addresses various topics including parallel computing and optimization techniques, tensor decomposition and applications, algorithms and data compression, computational physics and Python applications, embedded systems design techniques, advanced data storage technologies, as well as quantum chromodynamics and particle interactions.

Recent publications by Saman Amarasinghe and close collaborators demonstrate a range of interests and expertise. Notable papers include:

  • "A sparse iteration space transformation framework for sparse tensor algebra," 2020, Proceedings of the ACM on Programming Languages
  • "Variational study of two-nucleon systems with lattice QCD," 2023, Physical Review D
  • "A Deep Learning Based Cost Model for Automatic Code Optimization," 2021, arXiv (Cornell University)
  • "Compilation of sparse array programming models," 2021, Proceedings of the ACM on Programming Languages
  • "A Python-based programming language for high-performance computational genomics," 2021, Nature Biotechnology

The scientist frequently publishes in venues such as arXiv (Cornell University), Proceedings of the ACM on Programming Languages, and Physical Review D. Lesser but noted contributions also appear in Zenodo (CERN European Organization for Nuclear Research) and the American Journal of Agricultural Science Engineering and Technology.

Saman Amarasinghe has collaborated repeatedly with a number of researchers over their career. Frequent co-authors include Willow Ahrens, Stephen Y. Chou, Fredrik Kjølstad, Changwan Hong, and A. A. Y. Amarasinghe.

In 2019, Saman Amarasinghe received the ACM Fellow award for contributions to high performance computing on modern hardware platforms, domain-specific languages, and compilation techniques.

Best Publications

  • StreamIt: A Language for Streaming Applications

    William Thies;Michal Karczmarek;Saman P. Amarasinghe

  • The Raw microprocessor: a computational fabric for software circuits and general-purpose programs

    M.B. Taylor;J. Kim;J. Miller;D. Wentzlaff

  • Halide: a language and compiler for optimizing parallelism, locality, and recomputation in image processing pipelines

    Jonathan Ragan-Kelley;Connelly Barnes;Andrew Adams;Sylvain Paris

  • Baring it all to software: Raw machines

    E. Waingold;M. Taylor;D. Srikrishna;V. Sarkar

  • Maximizing multiprocessor performance with the SUIF compiler

    M.W. Hall;J.M. Anderson;J.M. Anderson;S.P. Amarasinghe;S.P. Amarasinghe;B.R. Murphy;B.R. Murphy

  • SUIF: an infrastructure for research on parallelizing and optimizing compilers

    Robert P. Wilson;Robert S. French;Christopher S. Wilson;Saman P. Amarasinghe

  • Secure Execution via Program Shepherding

    Vladimir Kiriansky;Derek Bruening;Saman P. Amarasinghe

  • Exploiting coarse-grained task, data, and pipeline parallelism in stream programs

    Michael I. Gordon;William Thies;Saman Amarasinghe

  • An infrastructure for adaptive dynamic optimization

    Derek Bruening;Timothy Garnett;Saman Amarasinghe

  • OpenTuner: an extensible framework for program autotuning

    Jason Ansel;Shoaib Kamil;Kalyan Veeramachaneni;Jonathan Ragan-Kelley

  • Evaluation of the Raw Microprocessor: An Exposed-Wire-Delay Architecture for ILP and Streams

    Michael Bedford Taylor;Walter Lee;Jason Miller;David Wentzlaff

  • Exploiting superword level parallelism with multimedia instruction sets

    Samuel Larsen;Saman Amarasinghe

  • Efficient, transparent, and comprehensive runtime code manipulation

    Derek L. Bruening;Saman Amarasinghe

  • Kendo: efficient deterministic multithreading in software

    Marek Olszewski;Jason Ansel;Saman Amarasinghe

  • Automatically patching errors in deployed software

    Jeff H. Perkins;Sunghun Kim;Sam Larsen;Saman Amarasinghe

  • PetaBricks: a language and compiler for algorithmic choice

    Jason Ansel;Cy Chan;Yee Lok Wong;Marek Olszewski

  • A stream compiler for communication-exposed architectures

    Michael I. Gordon;William Thies;Michal Karczmarek;Jasper Lin

  • The Tensor Algebra Compiler

    Fredrik Kjolstad;Shoaib Kamil;Stephen Chou;David Lugato

  • Data and computation transformations for multiprocessors

    Jennifer M. Anderson;Saman P. Amarasinghe;Monica S. Lam

  • Meta optimization: improving compiler heuristics with machine learning

    Mark Stephenson;Saman Amarasinghe;Martin Martin;Una-May O'Reilly

  • Maximizing Multiprocessor Performance with the SUIF Compiler.

    Mary W. Hall;Jennifer-Ann M. Anderson;Saman P. Amarasinghe;Brian R. Murphy

Frequent Co-Authors

William Thies
William Thies Microsoft (United States)
Shoaib Kamil
Shoaib Kamil Adobe Systems (United States)
Monica S. Lam
Monica S. Lam Stanford University
Mary Hall
Mary Hall University of Utah
Michael Taylor
Michael Taylor University of Washington
Henry Hoffmann
Henry Hoffmann University of Chicago
Matei Zaharia
Matei Zaharia University of California, Berkeley

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