Fastflow: High‐Level and Efficient Streaming on Multicore

Aldinucci, Marco; Danelutto, Marco; Kilpatrick, Peter; Torquati, Massimo

doi:10.1002/9781119332015.ch13

Cited by 114 publications

(92 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Benefits of skeleton-based compilation are, among others, the flexibility of extension to other targets and the performance potential: Optimizations can be performed in the native language within the skeletons. Examples of recent skeleton-based source-to-source compilers are Aldinucci et al [2013], Enmyren and Kessler [2010], and Steuwer et al [2011].…”

Section: Introductionmentioning

confidence: 99%

Bones

Nugteren

Corporaal

2014

ACM Trans. Archit. Code Optim.

View full text Add to dashboard Cite

The shift toward parallel processor architectures has made programming and code generation increasingly challenging. To address this programmability challenge, this article presents a technique to fully automatically generate efficient and readable code for parallel processors (with a focus on GPUs). This is made possible by combining algorithmic skeletons, traditional compilation, and "algorithmic species," a classification of program code. Compilation starts by automatically annotating C code with class information (the algorithmic species). This code is then fed into the skeleton-based source-to-source compiler BONES to generate CUDA code. To generate efficient code, BONES also performs optimizations including host-accelerator transfer optimization and kernel fusion. This results in a unique approach, integrating a skeleton-based compiler for the first time into an automated flow. The benefits are demonstrated experimentally for PolyBench GPU kernels, showing geometric mean speed-ups of 1.4× and 2.4× compared to PPCG and PAR4ALL, and for five Rodinia GPU benchmarks, showing a gap of only 1.2× compared to hand-optimized code.

show abstract

Section: Introductionmentioning

confidence: 99%

Bones

Nugteren

Corporaal

2014

ACM Trans. Archit. Code Optim.

View full text Add to dashboard Cite

show abstract

“…the Building blocks level) FastFlow CPU implementation of patterns are realised via non-blocking graphs of threads connected by way of lock-free channels [32], while the GPGPU implementation is realised by way of the CUDA/OpenCL bindings and offloading techniques [33]. The framework also takes care of memory transfers between CPU host and GPGPU device.…”

Section: The Fastflow Programming Frameworkmentioning

confidence: 99%

Parallel visual data restoration on multi-GPGPUs using stencil-reduce pattern

Aldinucci

Pezzi

Drocco

et al. 2015

The International Journal of High Performance Computing Applica

Self Cite

View full text Add to dashboard Cite

In this paper, a highly effective parallel filter for visual data restoration is presented. The filter is designed following a skeletal approach, using a newly proposed stencil-reduce, and has been implemented by way of the FastFlow parallel programming library. As a result of its high-level design, it is possible to run the filter seamlessly on a multicore machine, on multi-GPGPUs, or on both. The design and implementation of the filter are discussed, and an experimental evaluation is presented.

show abstract

“…Over the years, research on loop parallelism has been carried on using different approaches and techniques that vary from automatic parallelisation to iterations scheduling. In this paper we elected FastFlow [10] as a viable tool for re-writing NuChart. We then compared the results we have obtained, against OpenMP and TBB, which represent to a major extent the most widely used and studied frameworks for loop parallelisations.…”

mentioning

confidence: 99%

“…In OpenMP, two directives are used to parallelise a loop: the parallel directive declares a parallel region which will be executed concurrently by a pool of threads; the for directive is placed within the parallel region to distribute the loop iterations to the threads executing the parallel region. FastFlow is a parallel programming environment originally designed to support efficient streaming on cache-coherent multi-core platforms [10]. It is realised as a pattern-based C++ framework that provides a set of high-level programming patterns (aka algorithmic skeletons).…”

mentioning

confidence: 99%

NuChart-II: A Graph-Based Approach for Analysis and Interpretation of Hi-C Data

Tordini

Drocco

Merelli

et al. 2015

Computational Intelligence Methods for Bioinformatics and Biostatistics

Self Cite

View full text Add to dashboard Cite

Abstract. Long-range chromosomal associations between genomic regions, and their repositioning in the 3D space of the nucleus, are now considered to be key contributors to the regulation of gene expressions, and important links have been highlighted with other genomic features involved in DNA rearrangements. Recent Chromosome Conformation Capture (3C) measurements performed with high throughput sequencing (Hi-C) and molecular dynamics studies show that there is a large correlation between co-localization and co-regulation of genes, but these important researches are hampered by the lack of biologists-friendly analysis and visualisation software. In this work we present NuChart-II, a software that allows the user to annotate and visualize a list of input genes with information relying on Hi-C data, integrating knowledge data about genomic features that are involved in the chromosome spatial organization. This software works directly with sequenced reads to identify related Hi-C fragments, with the aim of creating gene-centric neighbourhood graphs on which multi-omics features can be mapped. NuChart-II is a highly optimized implementation of a previous prototype package developed in R, in which the graph-based representation of Hi-C data was tested. The prototype showed inevitable problems of scalability while working genome-wide on large datasets: particular attention has been paid in optimizing the data structures employed while constructing the neighbourhood graph, so as to foster an efficient parallel implementation of the software. The normalization of Hi-C data has been modified and improved, in order to provide a reliable estimation of proximity likelihood for the genes.

show abstract

Fastflow: High‐Level and Efficient Streaming on Multicore

Cited by 114 publications

References 19 publications

Bones

Bones

Parallel visual data restoration on multi-GPGPUs using stencil-reduce pattern

NuChart-II: A Graph-Based Approach for Analysis and Interpretation of Hi-C Data

Contact Info

Product

Resources

About