Metrics and models for reordering transformations

Strout, Michelle Mills; Hovland, Paul

doi:10.1145/1065895.1065899

Cited by 32 publications

(36 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Permutation RTRTs that SPF can represent include Cuthill-McKee [14], Reverse Cuthill-McKee [36], breadthfirst [2], Sloan [24], recursive coordinate bisection [70], consecutive packing [19], reordering based on graph partitioning [56,26], hybrid techniques based on graph partitioning and another heuristic within the partition [2,64], reordering based on space-filling curves [39], lexicographical grouping or sorting [15,19,24], and hyper-breadth-first [63]. The reordering algorithms that depend on a mapping of data indices to simulation space coordinate data (e.g., recursive coordinate bisection [70] and space-filling curves [39]) will require additional input be provided to the inspector, but this input can be expressed as an abstract relation.…”

Section: Data and Iteration Permutation Reorderingsmentioning

confidence: 99%

An approach for code generation in the Sparse Polyhedral Framework

et al. 2016

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Data and Iteration Permutation Reorderingsmentioning

confidence: 99%

An approach for code generation in the Sparse Polyhedral Framework

et al. 2016

Self Cite

View full text Add to dashboard Cite

show abstract

“…To model this affinity, we use a hypergraph H = (V, N ) where V is the set of vertices and N is the set of nets [43]. Iterations of all partitionable loops are represented by separate vertices i ∈ V. Each accessed element of a data array is represented by a net j ∈ N , whose pins are the iterations that access the data element.…”

Section: Partitioning the Computationmentioning

confidence: 99%

Code generation for parallel execution of a class of irregular loops on distributed memory systems

Ravishankar¹,

Eisenlohr²,

Pouchet³

et al. 2012

2012 International Conference for High Performance Computing, Networking, Storage and Analysis

View full text Add to dashboard Cite

Abstract-Parallelization and locality optimization of affine loop nests has been successfully addressed for shared-memory machines. However, many large-scale simulation applications must be executed in a distributed-memory environment, and use irregular/sparse computations where the control-flow and arrayaccess patterns are data-dependent.In this paper, we propose an approach for effective parallel execution of a class of irregular loop computations in a distributedmemory environment, using a combination of static and runtime analysis. We discuss algorithms that analyze sequential code to generate an inspector and an executor. The inspector captures the data-dependent behavior of the computation in parallel and without requiring complete replication of any of the data structures used in the original computation. The executor performs the computation in parallel. The effectiveness of the framework is demonstrated on several benchmarks and a climate modeling application.

show abstract

“…Here, we use the POSIX thread standard, which is supported across broad architectures and operating systems. In addition, our framework [2] includes the optimization of data and computation layouts [37,38], in which the computational cells are traversed along various spacefilling curves [39] (e.g. Hilbert or Morton curve).…”

Section: Tunable Hierarchical Cellular Decomposition (Hcd) For Algorimentioning

confidence: 99%