Automatic Calibration of Performance Models on Heterogeneous Multicore Architectures

Augonnet, Cédric; Thibault, Samuel; Namyst, Raymond

doi:10.1007/978-3-642-14122-5_9

Cited by 31 publications

(37 citation statements)

References 13 publications

(15 reference statements)

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“…The Cilk language 1 [5] allows task-focused parallel programming and is an early example of efficient task scheduling via work stealing. OpenMP [10], which we consider a language extension, integrates tasks into its programming interface since version 3.0.…”

Section: Stefano Markidis Markidis@kthsementioning

confidence: 99%

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A taxonomy of task-based parallel programming technologies for high-performance computing

et al. 2018

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Task-based programming models for shared memory-such as Cilk Plus and OpenMP 3-are well established and documented. However, with the increase in parallel, many-core, and heterogeneous systems, a number of research-driven projects have developed more diversified task-based support, employing various programming and runtime features. Unfortunately, despite the fact that dozens of different task-based systems exist today and are actively used for parallel and high-performance computing (HPC), no comprehensive overview or classification of task-based technologies for HPC exists. In this paper, we provide an initial task-focused taxonomy for HPC technologies, which covers both programming interfaces and runtime mechanisms. We

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Section: Stefano Markidis Markidis@kthsementioning

confidence: 99%

“…When targeting performance observation, performance monitoring software is either generating data to be used online [1,3,14,15,22] or offline [2,3,11,14]. In other words, whether the collected data are going to be used, while the application still runs or after its execution.…”

Section: Performance Monitoringmentioning

confidence: 99%

A taxonomy of task-based parallel programming technologies for high-performance computing

et al. 2018

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“…StarPU [21,20] is a C-based unified runtime system for heterogeneous multicore platforms with generic scheduling and selection facilities. Three main components of StarPU are task and codelet abstraction, data management, and dynamic scheduling and selection framework.…”

Section: Starpumentioning

confidence: 99%

Performance-aware component composition for GPU-based systems

Dastgeer¹

2014

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This thesis adresses issues associated with efficiently programming modern heterogeneous GPU-based systems, containing multicore CPUs and one or more programmable Graphics Processing Units (GPUs). We use ideas from component-based programming to address programming, performance and portability issues of these heterogeneous systems. Specifically, we present three approaches that all use the idea of having multiple implementations for each computation; performance is achieved/retained either a) by selecting a suitable implementation for each computation on a given platform or b) by dividing the computation work across different implementations running on CPU and GPU devices in parallel.In the first approach, we work on a skeleton programming library (SkePU) that provides high-level abstraction while making intelligent implementation selection decisions underneath either before or during the actual program execution. In the second approach, we develop a composition tool that parses extra information (metadata) from XML files, makes certain decisions offline, and, in the end, generates code for making the final decisions at runtime. The third approach is a framework that uses source-code annotations and program analysis to generate code for the runtime library to make the selection decision at runtime. With a generic performance modeling API alongside program analysis capabilities, it supports online tuning as well as complex program transformations.These approaches differ in terms of genericity, intrusiveness, capabilities and knowledge about the program source-code; however, they all demonstrate usefulness of component programming techniques for programming GPU-based systems. With experimental evaluation, we demonstrate how all three approaches, although different in their own way, provide good performance on different GPU-based systems for a variety of applications.This work has been supported by two EU FP7 projects (PEP-PHER, EXCESS) and by SeRC. Populärvetenskaplig sammanfattningAtt få varje generation av datorer att fungera snabbareär viktigt för samhä-llets utveckling och tillväxt. Traditionellt hade de flesta datorer bara en general-purpose processor (den så kallade CPU:n) som bara kunde exekvera en beräkningsuppgift i taget. Under det senasteårtiondet har dock flerkärniga och mångkärniga processorer blivit vanliga, och datorer har också blivit mer heterogena. Ett modernt datorsystem innehåller vanligtvis flerän en CPU, tillsammans med specialprocessorer såsom grafikprocessorer (GPU:er) som ar anpassade för att kunna exekvera vissa typer av beräkningar effektivarë an CPU:er. Vi kallar ett sådant system med en eller flera GPU:er för ett GPU-baserat system. GPU:er i sådana system har sitt eget separata minne, och för att kunna köra en beräkning på en GPU så behöver man vanligtvis flytta all indata till GPU:ns minne och sedan hämta tillbaka resultatet när beräkningenär klar.Programmeringen av GPU-baserade systemär icke-trivialt av flera anledningar: (1) CPU:er och GPU:er kräver olika programmeringsexper...

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“…Various scheduling techniques are presented, including greedy scheduling and performance-based scheduling. The performance estimations are either provided by the user or based on history information collected by the run-time [3].…”

Section: Related Workmentioning

confidence: 99%

A Static Task Partitioning Approach for Heterogeneous Systems Using OpenCL

Grewe

O’Boyle

2011

Lecture Notes in Computer Science

152

124

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Abstract. Heterogeneous multi-core platforms are increasingly prevalent due to their perceived superior performance over homogeneous systems. The best performance, however, can only be achieved if tasks are accurately mapped to the right processors. OpenCL programs can be partitioned to take advantage of all the available processors in a system. However, finding the best partitioning for any heterogeneous system is difficult and depends on the hardware and software implementation.We propose a portable partitioning scheme for OpenCL programs on heterogeneous CPU-GPU systems. We develop a purely static approach based on predictive modelling and program features. When evaluated over a suite of 47 benchmarks, our model achieves a speedup of 1.57 over a state-of-the-art dynamic run-time approach, a speedup of 3.02 over a purely multi-core approach and 1.55 over the performance achieved by using just the GPU.

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Automatic Calibration of Performance Models on Heterogeneous Multicore Architectures

Cited by 31 publications

References 13 publications

A taxonomy of task-based parallel programming technologies for high-performance computing

A taxonomy of task-based parallel programming technologies for high-performance computing

Performance-aware component composition for GPU-based systems

A Static Task Partitioning Approach for Heterogeneous Systems Using OpenCL

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