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author    = {Charles E. Leiserson and Neil C. Thompson and Joel S. Emer and Bradley C. Kuszmaul and Butler W. Lampson and Daniel Sanchez and Tao B. Schardl},
journal   = {Science},
loc       = {Science},
title     = {There's plenty of room at the Top: What will drive computer performance after Moore's law?},
year      = {2020},
month     = {jun},
number    = {6495},
pages     = {eaam9744},
volume    = {368},
doi       = {10.1126/science.aam9744},
publisher = {American Association for the Advancement of Science ({AAAS})},
url       = {}



author    = {Morihata, Akimasa and Sato, Shigeyuki},
title     = {Reverse Engineering for Reduction Parallelization via Semiring Polynomials},
year      = {2021},
isbn      = {9781450383912},
publisher = {Association for Computing Machinery},
address   = {New York, NY, USA},
url       = {},
abstract  = {Parallel reduction, which summarizes a given dataset, e.g., the total, average, and maximum, plays a crucial role in parallel programming. This paper presents a new approach, reverse engineering, to automatically discovering nontrivial parallel reductions in sequential programs. The body of the sequential reduction loop is regarded as a black box, and its input-output behaviors are sampled. If the behaviors correspond to a set of linear polynomials over a semiring, a divide-and-conquer parallel reduction is generated. Auxiliary reverse-engineering methods enable a long and nested loop body to be decomposed, which makes our parallelization scheme applicable to various types of reduction loops. This approach is not only simple and efficient but also agnostic to the details of the input program. Its potential is demonstrated through several use case scenarios. A proof-of-concept implementation successfully inferred linear polynomials for nearly all of the 74 benchmarks exhaustively collected from the literature. These characteristics and experimental results demonstrate the promise of the proposed approach, despite its inherent unsoundness.},
booktitle = {Proceedings of the 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation},
loc       = {Proceedings of the 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation},
number    = {2021},
pages     = {820–834},
numpages  = {15}



author    = {Jiang, Peng and Chen, Linchuan and Agrawal, Gagan},
title     = {Revealing Parallel Scans and Reductions in Recurrences through Function Reconstruction},
year      = {2018},
isbn      = {9781450359863},
publisher = {Association for Computing Machinery},
address   = {New York, NY, USA},
url       = {},
doi       = {10.1145/3243176.3243204},
abstract  = {Many sequential loops are actually recurrences and can be parallelized across iterations as scans or reductions. Many efforts over the past 2+ decades have focused on parallelizing such loops by extracting and exploiting the hidden scan/reduction patterns. These approaches have largely been based on a heuristic search for closed-form composition of computations across loop iterations.While the search-based approaches are successful in parallelizing many recurrences, they have a large search overhead and need extensive program analysis. In this work, we propose a novel approach called sampling-and-reconstruction, which avoids the search for closed-form composition and has the potential to cover more recurrence loops. It is based on an observation that many recurrences can have a point-value representation. The loop iterations are divided across processors, and where the initial value(s) of the recurrence variable(s) are unknown, we execute with several chosen (sampling) initial values. Then, correct final result can be obtained by reconstructing the function from the outputs produced on the chosen initial values. Our approach is effective in parallelizing linear, rectified-linear, finite-state and multivariate recurrences, which cover all of the test cases in previous works. Our evaluation shows that our approach can parallelize a diverse set of sequential loops, including cases that cannot be parallelized by a state-of-the-art static parallelization tool, and achieves linear scalability across multiple cores.},
booktitle = {Proceedings of the 27th International Conference on Parallel Architectures and Compilation Techniques},
loc       = {Proceedings of the 27th International Conference on Parallel Architectures and Compilation Techniques},
number    = {2018},
articleno = {10},
numpages  = {13},
keywords  = {loop parallelization, recurrence, reduction},
location  = {Limassol, Cyprus},
series    = {PACT '18}



title         = {Improved Parallel Cache-Oblivious Algorithms for Dynamic Programming and Linear Algebra}, 
author        = {Guy E. Blleloch and Yan Gu},
year          = {2019},
eprint        = {1809.09330},
archivePrefix = {arXiv},
primaryClass  = {cs.DS},
loc           = {arXiv},
number        = {1809.09330},
url           = {}



title         = {Compilation of Sparse Array Programming Models},
author        = {Rawn Henry, Olivia Hsu, Rohan Yadav, Stephen Chou, Kunle Olukotun, Saman Amarasinghe, and Fredrik Kjolstad},
year          = {2021},
articleno     = {128},
numpages      = {29},
url           = {},
publisher     = {Association for Computing Machinery},
loc           = {Proc. ACM Program. Lang. 5},
number        = {},
doi           = {10.1145/3485505}



author       = {Rajopadhye, Sanjay V. and Purushothaman, S. and Fujimoto, Richard M.},
editor       = {Nori, Kesav V.},
title        = {On synthesizing systolic arrays from Recurrence Equations with Linear Dependencies},
booktitle    = {Foundations of Software Technology and Theoretical Computer Science},
year         = {1986},
publisher    = {Springer Berlin Heidelberg},
address      = {Berlin, Heidelberg},
pages        = {488--503},
abstract     = {We present a technique for synthesizing systolic architectures from Recurrence Equations. A class of such equations (Recurrence Equations with Linear Dependencies) is defined and the problem of mapping such equations onto a two dimensional architecture is studied. We show that such a mapping is provided by means of a linear allocation and timing function. An important result is that under such a mapping the dependencies remain linear. After obtaining a two-dimensional architecture by applying such a mapping, a systolic array can be derived if the communication can be spatially and temporally localized. We show that a simple test consisting of finding the zeroes of a matrix is sufficient to determine whether this localization can be achieved by pipelining and give a construction that generates the array when such a pipelining is possible. The technique is illustrated by automatically deriving a well known systolic array for factoring a band matrix into lower and upper triangular factors.},
isbn         = {978-3-540-47239-1},
loc          = {Foundations of Software Technology and Theoretical Computer Science},
number       = {},
doi          = {10.1007/3-540-17179-7_30},
url          = {}



author       = {Mauras, C. and Quinton, P. and Rajopadhye, S. and Saouter, Y.},
booktitle    = {[1990] Proceedings of the International Conference on Application Specific Array Processors}, 
title        = {Scheduling affine parameterized recurrences by means of Variable Dependent Timing Functions}, 
year         = {1990},
volume       = {},
number       = {},
pages        = {100-110},
abstract     = {The authors present new scheduling techniques for systems of affine recurrence equations. They show that it is possible to extend earlier results on affine scheduling to the case when each variable of the system is scheduled independently of the others by an affine timing-function. This new technique makes it possible to analyze systems of recurrence equations with variables in different index spaces, and multi-step systolic algorithms. This theory applies directly to many problems, such as dynamic programming, LU decomposition, and 2-D convolution, and it avoids in particular preliminary heuristic rewriting of the equations.},
keywords     = {},
doi          = {10.1109/ASAP.1990.145447},
ISSN         = {},
month        = {Sep.},
loc          = {[1990] Proceedings of the International Conference on Application Specific Array Processors},
url          = {}



author       = {Mahdi Javanmard, Mohammad and Ahmad, Zafar and Zola, Jaroslaw and Pouchet, Louis-Noël and Chowdhury, Rezaul and Harrison, Robert},
booktitle    = {2020 IEEE International Conference on Cluster Computing (CLUSTER)}, 
title        = {Efficient Execution of Dynamic Programming Algorithms on Apache Spark}, 
year         = {2020},
volume       = {},
number       = {},
pages        = {337-348},
doi          = {10.1109/CLUSTER49012.2020.00044},
loc          = {[2020] IEEE International Conference on Cluster Computing (CLUSTER)},
url          = {}


melange/papers/fall2021.1633460750.txt.gz · Last modified: 2021/10/05 13:05 by corentin