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Quilleré, Fabien; Rajopadhye, Sanjay; Wilde, Doran

doi:10.1023/a:1007554627716

Cited by 124 publications

(10 citation statements)

References 28 publications

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“…The algorithm used to transform a polyhedral program with lexicographic schedule into a PolyLoop program is shown in Figure 6. It is based on the algorithm by Quilleré et al [2000]. An important difference is that there is no context in which we generate the syntax tree, and that we do not simplify loop bounds in this context.…”

Section: Decomposition Into Loop Nestsmentioning

confidence: 99%

“…Indeed, in the PolyLoop program, the loops that will be generated need to be ordered; however, this means that in every environment, one of these loops will always be executed before the other. Quilleré et al [2000] show that it is always possible to order two disjoint polyhedra to respect the desired execution order. The property łP 1 can be ordered before P 2 ž is expressed as:…”

Section: Decomposition Into Loop Nestsmentioning

confidence: 99%

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Verified code generation for the polyhedral model

Courant

Leroy

2021

Proc. ACM Program. Lang.

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The polyhedral model is a high-level intermediate representation for loop nests that supports elegantly a great many loop optimizations. In a compiler, after polyhedral loop optimizations have been performed, it is necessary and difficult to regenerate sequential or parallel loop nests before continuing compilation. This paper reports on the formalization and proof of semantic preservation of such a code generator that produces sequential code from a polyhedral representation. The formalization and proofs are mechanized using the Coq proof assistant.

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Section: Decomposition Into Loop Nestsmentioning

confidence: 99%

Section: Decomposition Into Loop Nestsmentioning

confidence: 99%

Verified code generation for the polyhedral model

Courant

Leroy

2021

Proc. ACM Program. Lang.

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“…Code generation is the task of converting a polyhedral model of a program with a schedule and storage optimization functions into imperative code. Using algorithms such as (Grosser et al 2015;Quilleré et al 2000), an abstract syntax tree (AST) is generated, which contains loops and conditional statements that visit each point in a polyhedral schedule and execute the associated statement instances. The induction variables (iterators) of the loops represent coordinates of the schedule points, and they are ultimately mapped to array indices through the storage optimization functions.…”

Section: Storage Allocation and Code Generationmentioning

confidence: 99%

Polyhedral Compilation for Multi-dimensional Stream Processing

Leben

Tzanetakis

2019

ACM Trans. Archit. Code Optim.

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We present a method for compilation of multi-dimensional stream processing programs from affine recurrence equations with unbounded domains into imperative code with statically allocated memory. The method involves a novel polyhedral schedule transformation called periodic tiling. It accommodates existing polyhedral optimizations to improve memory access patterns and expose parallelism. This enables efficient execution of programming languages with unbounded recurrence equations, as well as optimization of existing languages from which this form can be derived. The method is experimentally evaluated on 5 DSP algorithms with large problem sizes. Results show potential for improved throughput compared to hand-optimized C++ (speedups on a 6-core Intel Xeon CPU up to 10× with a geometric mean 3.3×). 1

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“…The first one was developed by Quillere and al. [14] and later extended by Bastoul in the context of the ClooG software [3]. ClooG allows regenerated loops to be guardless, thus avoiding useless iterations at the price of an increase in code size.…”

Section: Code Generationmentioning

confidence: 99%

Efficient nested loop pipelining in high level synthesis using polyhedral bubble insertion

Morvan

Derrien

Quinton

2011

2011 International Conference on Field-Programmable Technology

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Loop pipelining is a key transformation in highlevel synthesis tools as it helps maximizing both computational throughput and hardware utilization. Nevertheless, it somewhat looses its efficiency when dealing with small trip-count inner loops, as the pipeline latency overhead quickly limits its efficiency. Even if it is possible to overcome this limitation by pipelining the execution of a whole loop nest, the applicability of nested loop pipelining has so far been limited to a very narrow subset of loops, namely perfectly nested loops with constant bounds. In this work we propose to extend the applicability of nested-loop pipelining to imperfectly nested loops with affine dependencies by leveraging on the so-called polyhedral model. We show how such loop nest can be analyzed, and under certain conditions, how one can modify the source code in order to allow nested loop pipeline to be applied using a method called polyhedral bubble insertion. We also discuss the implementation of our method in a source-to-source compiler specifically targeted at High-Level Synthesis tools.

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Cited by 124 publications

References 28 publications

Verified code generation for the polyhedral model

Verified code generation for the polyhedral model

Polyhedral Compilation for Multi-dimensional Stream Processing

Efficient nested loop pipelining in high level synthesis using polyhedral bubble insertion

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