Kleanthis Psarris

Brooklyn College

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Nectarios Koziris

National Technical University of Athens

Chia‐Tien Dan Lo

Southern Polytechnic State University

Robert van Engelen

Florida State University

Yi-Gang Tai

The University of Texas at San Antonio

Nikos E. Mastorakis

Technical University of Sofia

Kostas J. Kyriakopoulos

National Technical University of Athens

Valeri Mladenov

Technical University of Sofia

David Klappholz

Stevens Institute of Technology

Santosh Pande

Georgia Institute of Technology

David Niedzielski

Massey University

Cooperative Institutions

The University of Texas at San Antonio

National Technical University of Athens

National and Kapodistrian University of Athens

Georgia Institute of Technology

University of Colorado Colorado Springs

Technical University of Sofia

Hellenic Naval Academy

University of West Attica

University of Thessaly

Ural Federal University

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Parallel and Distributed Systems and Networking Track Editorial.

Robert van Engelen Turgay Korkmaz Nectarios Koziris Kleanthis Psarris

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On the accuracy of the Banerjee test

Journal of Parallel and Distributed Computing (1991)

Kleanthis Psarris David Klappholz Xiangyun Kong

Vectorization (mathematics)

Code (set theory)

10.1016/0743-7315(91)90019-6

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Citations (30)

On exact data dependence analysis

Kleanthis Psarris

The GCD test and the Banerjee-Wolfe test are the two tests traditionally used to determine statement data dependence, subject to direction vectors, in automatic vectorization / parallelization of loops. In an earlier study [14] a sufficient condition for the accuracy of the Banerjee-Wolfe test was stated and proved. In the original presentation only the case of general data dependence was considered, i.e., the case of data dependence without direction vector information. In this paper we extend the previous work to the case of data dependence subject to an arbitrary direction vector. We also state and prove a sufficient condition for the accuracy of a combination of the GCD and the Banerjee-Wolfe test. Finally, we demonstrate how these results can be used in actual practice to obtain exact data dependence information.

Vectorization (mathematics)

10.1145/143369.143424

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Citations (5)

The Banerjee–Wolfe and GCD Tests on Exact Data Dependence Information

Journal of Parallel and Distributed Computing (1996)

Kleanthis Psarris

Vectorization (mathematics)

10.1006/jpdc.1996.0009

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Keynote lecture 3: compilation and optimization for high performance computing

Kleanthis Psarris

High end parallel and multi-core processors rely on compilers to perform the necessary optimizations and exploit concurrency in order to achieve higher performance. However, source code for high performance computers is extremely complex to analyze and optimize. In particular, program analysis techniques often do not take into account complex expressions during the data dependence analysis phase. Most data dependence tests are only able to analyze linear expressions, even though non-linear expressions occur very often in practice. Therefore, considerable amounts of potential parallelism remain unexploited. In this talk we propose new data dependence analysis techniques to handle such complex instances of the dependence problem and increase program parallelization. Our method is based on a set of polynomial time techniques that can prove or disprove dependences in source codes with non-linear and symbolic expressions, complex loop bounds, arrays with coupled subscripts, and if-statement constraints. In addition our algorithm can produce accurate and complete direction vector information, enabling the compiler to apply further transformations. To validate our method we performed an experimental evaluation and comparison against the I-Test, the Omega test and the Range test in the Perfect and SPEC benchmarks. The experimental results indicate that our dependence analysis tool is accurate, efficient and more effective in program parallelization than the other dependence tests. The improved parallelization results into higher speedups and better program execution performance in several benchmarks.

Dependence analysis

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Automatic parallelization

Program optimization

Statement (logic)

Multi-core processor

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Synthesizing Tiled Matrix Decomposition on FPGAs

Yi-Gang Tai Kleanthis Psarris Chia‐Tien Dan Lo

Hardware accelerators such as FPGAs and GPUs in heterogeneous systems are being increasingly important for many applications. For high performance computing, the flexibility and efficiency of FPGA makes it an attractive alternative to other approaches. However, for applications that have to decompose large matrices, not many scalable solutions can be found on FPGAs. In this paper, we propose a scalable QR matrix decomposer on FPGAs based on the latest advances in tiled matrix decomposition algorithms for high performance linear algebra. The proposed design can decompose a matrix of size limited only by off-chip memory and has the potential to achieve high performance.

QR decomposition

Matrix (chemical analysis)

Linear algebra

Reconfigurable Computing

10.1109/fpl.2011.91

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Citations (6)

Measuring the Accuracy and Efficiency of the Data Dependence Tests.

Parallel and Distributed Computing Systems (ISCA) (2001)

Kleanthis Psarris Konstantinos G. Kyriakopoulos

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Data dependence analysis for complex loop regions

International Conference on Parallel Processing (2001)

Kostas J. Kyriakopoulos Kleanthis Psarris

Parallelizing compilers rely on data dependence information in order to produce valid parallel code. Polynomial data dependence analysis techniques, such as the Banerjee test and the I-Test, can efficiently compute data dependence information for simple instances of the data dependence problem. In more complicated cases such as triangular or trapezoidal loop regions with direction vector constraints these tests, including the triangular Banerjee test, ignore or simplify many of the constraints and thus introduce further approximations. The I-Test and the Omega test are two data dependence tests that can provide exact data dependence information. In addition the Omega test can accurately handle complex loop regions but at a higher computation cost. We extend the ideas behind the I-Test to handle such complex regions which are frequently found in actual source code. In particular, we provide a polynomial-time algorithm, the VI-Test that can detect data dependences in loops with triangular bounds and symbolic variables subject to any direction vector. We also perform an extensive experimental evaluation of the various dependence tests, including the I-Test, the VI-Test and the Omega test. We run several experiments using the Perfect Club Benchmarks and the scientific libraries Eispack, Linpack and Lapack. We present accuracy results, reasons for inconclusive answers, and comparative efficiency metrics.

Test suite

Code (set theory)

10.1109/icpp.2001.952063

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Citations (4)

Keynote lecture 2: program analysis and optimization for multi-core computing

International Conference on Systems (2011)

Kleanthis Psarris

As multi-core architectures become ubiquitous in modern computing, large scale scientific applications have to be redesigned to efficiently use the multiple cores and deliver higher performance. One major approach is the automatic detection of parallelism, in which existing conventional sequential programs are translated into parallel programs by optimizing compilers, in order to take advantage of the multiple processors. Optimizing compilers rely upon program analysis techniques to detect data dependences between program statements, perform optimizations, and identify code fragments that can be executed in parallel. In this work we study various program analysis and optimization techniques for multi-core computing and measure their impact in practice. We perform an experimental evaluation of several data dependence tests and program analysis techniques and we compare them in terms of data dependence accuracy, compilation efficiency, effectiveness in parallelization and program execution performance. We run various experiments using the Perfect Club Benchmarks, the SPEC benchmarks, and the scientific library Lapack. We present the measured accuracy of each data dependence test and explain the reasons for inaccuracies. We compare these tests in terms of efficiency and we analyze the tradeoffs between accuracy and efficiency. We also determine the impact of each data dependence test on the total compilation time. Finally, we measure the number of loops parallelized by each test and we compare the execution performance of each benchmark on a multicore architecture.

Benchmark (surveying)

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Multi-core processor

Program optimization

Code (set theory)

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An Analytical Evaluation of Data Dependence Analysis Techniques

International Journal of Parallel Programming (2018)

David Niedzielski Kleanthis Psarris Theoharis Theoharis

Theory of computation

10.1007/s10766-018-0577-7

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Citations (1)