razya ladelsky - automatic parallelization graphite -

ACOTES Project: Advanced Compiler Technologies for

Razya Ladelsky 2 Menno Lindwer 6 Xavier Martorell 5 Cupertino Miranda 3 Dorit Nuzman 2 Andrea Ornstein 4 Antoniu Pop 7 Sebastian Pop 8 Louis-Nol Pouchet 3 Alex Ramrez 5 David Rdenas 5 Erven Rohou 4 Ira Rosen 2 Uzi Shvadron 2 Konrad Trifunović 3

Automatic parallelization in GCC

Automatic parallelization distributes sequential code into multi-threaded code. It automatically generates parallel (multi-threaded) code for specific loop constructs using the gomp library. The first version of the code, allowing parallelization of inner-most loops that carry no dependences, was contributed by Zdenek Dvorak and Sebastian Pop (integrated to GCC 4.3).

Parallelizing more Loops with Compiler Guided Refactoring

Parallelizing More Loops with Compiler Guided Refactoring Per Larsen∗, Razya Ladelsky‡, Jacob Lidman †, Sally A. McKee, Sven Karlsson∗ and Ayal Zaks ∗DTU Informatics, Technical Univ. of Denmark, 2800 Kgs.Lyngby, Denmark Email: {pl,ska}imm.dtu.dk

BUGSENG

The theoretical framework for the automatic parallelization of ACLs is well established. However, the hardware compilation of arbitrary ACLs is still in its infancy. An important component for an efficient hardware implementation is a control mechanism that informs each processing element (PE) which computation needs to be performed and when.

Feedback Driven Annotation and Refactoring of Parallel Programs

[98]Per Larsen, Razya Ladelsky, Jacob Lidman, Sally A. McKee, Sven Karls-son and Ayal Zaks. Automatic Loop Parallelization via Compiler Guided Refactoring. Technical Report, IMM-Technical Report-2011-12, DTU In-formatics, Technical University of dtu.dk

Parallelizing more Loops with Compiler Guided Refactoring

Parallelizing More Loops with Compiler Guided Refactoring Per Larsen∗, Razya Ladelsky‡, Jacob Lidman †, Sally A. McKee, Sven Karlsson∗ and Ayal Zaks ∗DTU Informatics, Technical Univ. of Denmark, 2800 Kgs.Lyngby, Denmark Email: {pl,ska}imm.dtu.dk

Automatic Loop Parallelization via Compiler Guided Refactoring

Automatic Loop Parallelization via Compiler Guided Refactoring Per Larsen, Razya Ladelskyz, Jacob Lidman y, Sally A. McKee, Sven Karlsson and Ayal Zaksz DTU Informatics Technical U. Denmark, 2800 Kgs. Lyngby, Denmark Email: fpl,skagimm.dtu.dk yComputer Science Engineering

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A Bibliography of Publications about Virtual Machines Nelson H. F. Beebe University of Utah Department of Mathematics, 110 LCB 155 S 1400 E RM 233 Salt Lake City, UT 84112-0090 USA Tel: +1 801 581 5254 FAX: +1 801 581 4148 E-mail: [email protected], [email protected], [email protected]

BUGSENG

The theoretical framework for the automatic parallelization of ACLs is well established. However, the hardware compilation of arbitrary ACLs is still in its infancy. An important component for an efficient hardware implementation is a control mechanism that informs each processing element (PE) which computation needs to be performed and when.

ACM SIG{}PLAN Notices

ACM SIG{}PLAN Notices Volume 45, Number 5, May, 2010 Tilak Agerwala Exascale computing: the challenges and opportunities in the next decade . . . . 1--2 Mario Mndez-Lojo and Donald Nguyen and Dimitrios Prountzos and Xin Sui and M. Amber Hassaan and Milind Kulkarni and Martin Burtscher and Keshav Pingali Structure-driven optimizations for amorphous data-parallel programs . . . .

Computer Architecture (Computer Systems Design sub

Eliminating Global Interpreter Locks in Ruby through Hardware Transactional Memory Rei Odaira, Jose G. Castanos, and Hisanobu Tomari Proceedings of the 19th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP), pp. 131--142, 2014

GRAPHITE Two Years After: First Lessons Learned From Real

GRAPHITE Two Years After First Lessons Learned From Real-World Polyhedral Compilation Konrad Trifunovic2, Albert Cohen2, David Edelsohn3, Li Feng6, Tobias Grosser5, Harsha Jagasia1, Razya Ladelsky4, Sebastian Pop1, Jan Sjodin1, and Ramakrishna

(PDF) Graphite two years after: First lessons learned from

Graphite two years after: First lessons learned from real-world polyhedral compilation 2010 David Edelsohn K. Trifunovic H. Jagasia Automatic speculative parallelization of loops using polyhedral dependence analysis By arnamoy bhattacharyya By,,,, and

ACOTES Project: Advanced Compiler Technologies for Embedded Streaming

Int J Parallel Prog DOI 10.1007/s10766-010-0132-7 ACOTES Project: Advanced Compiler Technologies for Embedded Streaming Harm Munk Eduard Ayguad Cdric Bastoul Paul Carpenter Zbigniew Chamski Albert Cohen Marco Cornero Philippe Dumont Marc Duranton

GCC To Receive Automatic Parallelization Support

IBM's Razya Ladelsky today outlined plans for providing automatic parallelization support within the GNU Compiler Collection. The Graphite Framework, which provides high-level loop optimizations based upon the polyhedral model, was merged for the forthcoming release of GCC 4.4 and it will be used eventually to provide some level of automatic parallelization support.

Automatic Loop Parallelization via Compiler Guided Refactoring

Automatic Loop Parallelization via Compiler Guided Refactoring Per Larsen, Razya Ladelskyz, Jacob Lidman y, Sally A. McKee, Sven Karlsson and Ayal Zaksz DTU Informatics Technical U. Denmark, 2800 Kgs. Lyngby, Denmark Email: fpl,skagimm.dtu.dk yComputer Science Engineering

Scalable Multi

Therefore, we consider to implement an automatic table update mechanism triggered by a change to BADDR. 1.4.2 Virtual Address Space The current version of the DME also supports a virtual address space giving flexibility to application programmers and compilers because the addresses used in a program can be kept separate of other programs and independent of where in the system the program

Scalable Multi

Therefore, we consider to implement an automatic table update mechanism triggered by a change to BADDR. 1.4.2 Virtual Address Space The current version of the DME also supports a virtual address space giving flexibility to application programmers and compilers because the addresses used in a program can be kept separate of other programs and independent of where in the system the program

[PDF] Compiler Driven Code Comments and Refactoring

With our approach, automatic parallelization yields 8.6 best-case speedup over the sequential version. The transformations and suggestions are based on the loop parallelization and matrix reorganization capabilities in the latest production release of gcc.

[PDF] Compiler Driven Code Comments and Refactoring

With our approach, automatic parallelization yields 8.6 best-case speedup over the sequential version. The transformations and suggestions are based on the loop parallelization and matrix reorganization capabilities in the latest production release of gcc.

DeepDyve

ACOTES Project: Advanced Compiler Technologies for Embedded Streaming ACOTES Project: Advanced Compiler Technologies for Embedded Streaming Munk, Harm; Ayguad, Eduard; Bastoul, Cdric; Carpenter, Paul; Chamski, Zbigniew; Cohen, Albert; Cornero, Marco; Dumont, Philippe; Duranton, Marc; Fellahi, Mohammed; Ferrer, Roger; Ladelsky, Razya; Lindwer, Menno; Martorell, Xavier;

BUGSENG

The theoretical framework for the automatic parallelization of ACLs is well established. However, the hardware compilation of arbitrary ACLs is still in its infancy. An important component for an efficient hardware implementation is a control mechanism that informs each processing element (PE) which computation needs to be performed and when.

ACOTES: Advanced Compiler Technologies for Embedded Streaming

Razya Ladelsky, Dorit Nuzman, Ira Rosen, Uzi Shvadron, Ayal Zaks IBM Haifa Research Laboratories Albert Cohen, Cedric Bastoul, centrate our efforts on support for parallelization across several levels. Data streaming applications typically contain potential

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