A unifying framework for iteration reordering transformations

Kelly, Wayne; Pugh, William

doi:10.1109/icapp.1995.472180

Cited by 69 publications

(78 citation statements)

References 11 publications

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“…This is not the case with multidimensional schedules, when using at least as many dimensions as the original domain [9]. Moreover, using additional dimensions to explicitly order different statements onto a given dimension makes transformation manipulation easier [14,6]. As a result, multidimensional schedules with more dimensions than original domains are quite often used to specify transformations.…”

Section: Scalar Dimensionsmentioning

confidence: 99%

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Polyhedral Code Generation in the Real World

Vasilache

Bastoul

Cohen

2006

Lecture Notes in Computer Science

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Abstract. The polyhedral model is known to be a powerful framework to reason about high level loop transformations. Recent developments in optimizing compilers broke some generally accepted ideas about the limitations of this model. First, thanks to advances in dependence analysis for irregular access patterns, its applicability which was supposed to be limited to very simple loop nests has been extended to wide code regions. Then, new algorithms made it possible to compute the target code for hundreds of statements while this code generation step was expected not to be scalable. Such theoretical advances and new software tools allowed actors from both academia and industry to study more complex and realistic cases. Unfortunately, despite strong optimization potential of a given transformation for e.g., parallelism or data locality, code generation may still be challenging or result in high control overhead. This paper presents scalable code generation methods that make possible the application of increasingly complex program transformations. By studying the transformations themselves, we show how it is possible to benefit from their properties to dramatically improve both code generation quality and space/time complexity, with respect to the best stateof-the-art code generation tool. In addition, we build on these improvements to present a new algorithm improving generated code performance for strided domains and reindexed schedules.

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Section: Scalar Dimensionsmentioning

confidence: 99%

“…Scheduling policy Unified transformation frameworks like UTF [14] or URUK [6] are good example of multi-dimensional schedule policies. Both ask for (2ρ(S) + 1) dimensions which allow them to be much more flexible.…”

Section: Scalar Dimensionsmentioning

confidence: 99%

Polyhedral Code Generation in the Real World

Vasilache

Bastoul

Cohen

2006

Lecture Notes in Computer Science

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“…Currently, the dominant transformation framework for affine transformations is the polyhedral framework [72,23,54,31,13,8,48,6]. There are two reasons these techniques cannot be applied when there are indirect memory references.…”

Section: Introductionmentioning

confidence: 99%

“…There have been many program optimizations and transformation frameworks developed for improving the memory reference patterns for codes that are limited to affine references [23,37,54,34,10,31,32,73,29,12,66]. Currently, the dominant transformation framework for affine transformations is the polyhedral framework [72,23,54,31,13,8,48,6].…”

Section: Introductionmentioning

confidence: 99%

An approach for code generation in the Sparse Polyhedral Framework

et al. 2016

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Applications that manipulate sparse data structures contain memory reference patterns that are unknown at compile time due to indirect accesses such as A [B[i]]. To exploit parallelism and improve locality in such applications, prior work has developed a number of run-time reordering transformations (RTRTs). This paper presents the Sparse Polyhedral Framework (SPF) for specifying RTRTs and compositions thereof and algorithms for automatically generating efficient inspector and executor code to implement such transformations. Experimental results indicate that the performance of automatically generated inspectors and executors competes with the performance of hand-written ones in some cases.

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“…Guidance requires an infrastructure that supports queries of the form, "under what circumstances should I apply this transformation?" [35,29,2,18,33]. Answering these queries in the face of complicated program structures, unknown target architecture, and lack of knowledge of the input data requires a combined compile-time/runtime solution.…”

Section: Introductionmentioning

confidence: 99%

A Modal Model of Memory

Mitchell

Carter

Ferrante

2001

Computational Science — ICCS 2001

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Abstract. We consider the problem of automatically guiding program transformations for locality, despite incomplete information due to complicated program structures, changing target architectures, and lack of knowledge of the properties of the input data. Our system, the modal model of memory, uses limited static analysis and bounded runtime experimentation to produce performance formulas that can be used to make runtime locality transformation decisions. Static analysis is performed once per program to determine its memory reference properties, using modes, a small set of parameterized, kernel reference patterns. Once per architectural system, our system automatically performs a set of experiments to determine a family of kernel performance formulas. The system can use these kernel formulas to synthesize a performance formula for any program's mode tree. Finally, with program transformations represented as mappings between mode trees, the generated performance formulas can be used to guide transformation decisions.

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A unifying framework for iteration reordering transformations

Cited by 69 publications

References 11 publications

Polyhedral Code Generation in the Real World

Polyhedral Code Generation in the Real World

An approach for code generation in the Sparse Polyhedral Framework

A Modal Model of Memory

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