Telescoping Languages: A System for Automatic Generation of Domain Languages

Kennedy, Ken; Broom, Bradley M.; Chauhan, Arun; Fowler, Robert J.; Garvin, J.; Koelbel, Charles; McCosh, Cheryl; Mellor-Crummey, John

doi:10.1109/jproc.2004.840447

Cited by 58 publications

(27 citation statements)

References 51 publications

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“…In the future, we will investigate an adaptation of our algorithm to an interprocedural, modular compiler using the Telescoping Languages approach [16], which includes a size-inference algorithm that infers sizes of procedure variables in terms of the sizes of input arguments. This information will be important for determining benefits for in-placeness when the whole program is not available.…”

Section: Discussionmentioning

confidence: 99%

Scheduling Tasks to Maximize Usage of Aggregate Variables in Place

Abu-Mahmeed

McCosh

Budimlić

et al. 2009

Lecture Notes in Computer Science

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Abstract. Single-assignment languages with copy semantics have a very simple and approachable programming model. A naïve implementation of the copy semantics that copies the result of every computation to a new location, can result in poor performance. Whereas, an implementation that keeps the results in the same location, when possible, can achieve much higher performance.In this paper, we present a greedy algorithm for in-place computation of aggregate (array and structure) variables. Our algorithm greedily picks the most profitable opportunities for in-place computation, then updates the scheduling and in-place constraints in the program graph. The algorithm runs in O(T logT + EW V + V 2 ) time, where T is the number of in-placeness opportunities, EW is the number of edges and V the number of computational nodes in a program graph.We evaluate the performance of the code generated by the LabVIEW TM compiler using our algorithm against the code that performs no in-place computation at all, resulting in significant application performance improvements. We also compare the performance of the code generated by our algorithm against the commercial LabVIEW compiler that uses an adhoc in-placeness strategy. The results show that our algorithm matches the performance of the current LabVIEW strategy in most cases, while in some cases outperforming it significantly.

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Section: Discussionmentioning

confidence: 99%

Scheduling Tasks to Maximize Usage of Aggregate Variables in Place

Abu-Mahmeed

McCosh

Budimlić

et al. 2009

Lecture Notes in Computer Science

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“…Our work differs in that the input is matrix algebra (instead of loops), and we optimize sequences of operations for memory efficiency. The telescoping languages project [29] analyzes MATLAB scripts and optimizes them using procedure specialization, strength reduction, and vectorization. Similarly, Broadway [26] optimizes calls to libraries such as PLA-PACK [4].…”

Section: Background and Related Workmentioning

confidence: 99%

Automating the generation of composed linear algebra kernels

Belter

Jessup

Karlin

et al. 2009

Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis

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Memory bandwidth limits the performance of important kernels in many scientific applications. Such applications often use sequences of Basic Linear Algebra Subprograms (BLAS), and highly efficient implementations of those routines enable scientists to achieve high performance at little cost. However, tuning the BLAS in isolation misses opportunities for memory optimization that result from composing multiple subprograms. Because it is not practical to create a library of all BLAS combinations, we have developed a domain-specific compiler that generates them on demand. In this paper, we describe a novel algorithm for compiling linear algebra kernels and searching for the best combination of optimization choices. We also present a new hybrid analytic/empirical method for quickly evaluating the profitability of each optimization. We report experimental results showing speedups of up to 130% relative to the GotoBLAS on an AMD Opteron and up to 137% relative to MKL on an Intel Core 2.

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“…The Telescoping language project [7,15] was aimed to develop a framework for automatically generating custom optimizing compilers for domainspecific languages and libraries. ROSE, on the other hand, uses a more pragmatic approach to allow average programmers to write customized analyses and optimizations for abstractions.…”

Section: Related Workmentioning

confidence: 99%

Semantic-Aware Automatic Parallelization of Modern Applications Using High-Level Abstractions

Liao

Quinlan

Willcock

et al. 2010

Int J Parallel Prog

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Automatic introduction of OpenMP for sequential applications has attracted significant attention recently because of the proliferation of multicore processors and the simplicity of using OpenMP to express parallelism for shared-memory systems. However, most previous research has only focused on C and Fortran applications operating on primitive data types. Modern applications using high-level abstractions, such as C++ STL containers and complex user-defined class types, are largely ignored due to the lack of research compilers that are readily able to recognize highlevel object-oriented abstractions and leverage their associated semantics. In this paper, we use a source-to-source compiler infrastructure, ROSE, to explore compiler techniques to recognize high-level abstractions and to exploit their semantics for automatic parallelization. Several representative parallelization candidate kernels are used to study semantic-aware parallelization strategies for high-level abstractions, combined with extended compiler analyses. Preliminary results have shown that semantics of abstractions can help extend the applicability of automatic parallelization to modern applications and expose more opportunities to take advantage of multicore processors.

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Telescoping Languages: A System for Automatic Generation of Domain Languages

Abstract: The software gap-the discrepancy between the need for new software and the aggregate capacity of the workforce to produce it-is a serious problem for scientific software. Although users appreciate the convenience (and, thus, improved productivity)

Cited by 58 publications

References 51 publications

Scheduling Tasks to Maximize Usage of Aggregate Variables in Place

Scheduling Tasks to Maximize Usage of Aggregate Variables in Place

Automating the generation of composed linear algebra kernels

Semantic-Aware Automatic Parallelization of Modern Applications Using High-Level Abstractions

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