Binding Nested OpenMP Programs on Hierarchical Memory Architectures

Schmidl, Dirk; Terboven, Christian; Mey, Dieter an; Bücker, H. Martin

doi:10.1007/978-3-642-13217-9_3

Cited by 6 publications

(4 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By sharing the same mainboard, the devices avoid having to move information through an external network, thereby reducing the latency and overload associated with parallelism. The additional benefit obtained from utilizing OpenMP is that it can be employed as a controller and communicator for the integrated boards at the same node in a nested parallelism scheme [24].…”

Section: Design Of the Parallel Algorithmmentioning

confidence: 99%

A Performance Study of a Dual Xeon-Phi Cluster for the Forward Modelling of Gravitational Fields

Couder-Castañeda

Trujillo-Alcántara

Herrera-Diaz

et al. 2015

Scientific Programming

View full text Add to dashboard Cite

With at least 60 processing cores, the Xeon-Phi coprocessor is a truly multicore architecture, which consists of an interconnection speed among cores of 240 GB/s, two levels of cache memory, a theoretical performance of 1.01 Tflops, and programming flexibility, all making the Xeon-Phi an excellent coprocessor for parallelizing applications that seek to reduce computational times. The objective of this work is to migrate a geophysical application designed to directly calculate the gravimetric tensor components and their derivatives and in this way research the performance of one and two Xeon-Phi coprocessors integrated on the same node and distributed in various nodes. This application allows the analysis of the design factors that drive good performance and compare the results against a conventional multicore CPU. This research shows an efficient strategy based on nested parallelism using OpenMP, a design that in its outer structure acts as a controller of interconnected Xeon-Phi coprocessors while its interior is used for parallelyzing the loops. MPI is subsequently used to reduce the information among the nodes of the cluster.

show abstract

Section: Design Of the Parallel Algorithmmentioning

confidence: 99%

A Performance Study of a Dual Xeon-Phi Cluster for the Forward Modelling of Gravitational Fields

Couder-Castañeda

Trujillo-Alcántara

Herrera-Diaz

et al. 2015

Scientific Programming

View full text Add to dashboard Cite

show abstract

“…It focus on data placement and movement but do not deals with number of threads. [16] and [17] have also proposed to extend OpenMP for controlling task placement, but both of them requires programmers to set number of threads for nested parallel regions explicitly. Algorithms to distribute threads over different tasks have been studied in [4], but they have not tried to integrate such algorithms into programming language.…”

Section: Related Workmentioning

confidence: 99%

NestedMP: Taming Complex Configuration Space of Degree of Parallelism for Nested-Parallel Programs

Chen

Tang

2014

2014 43rd International Conference on Parallel Processing Workshops

View full text Add to dashboard Cite

It is beneficial to exploit multiple levels of parallelism for a wide range of applications, because a typical server already has tens of processor cores now. As the number of cores in a computer is increasing rapidly, efficient support of nested parallelism will be more important. However, compared to single-level parallelism, nested-parallelism is much more complicated for programming since its configuration space of degree of parallelism is more complicated. Nowadays parallel programming models such as OpenMP only have naive support for nested parallelism, and programmers need to specify number of threads for each parallel task explicitly to get a reasonable performance. Such method has two drawbacks. First, it is a complicated job to write code to figure out appropriate configurations for different environments and contexts. Second, the runtime system lacks sufficient global information about threads allocation to make optimal decision on task-core mapping, which easily causes significant performance loss.To deal with such problems, we propose NestedMP, a set of directives which extends OpenMP. NestedMP adopts a model that propagate available threads on task tree in a topdown way, which provides global information about threads allocation for runtime system when high level parallel tasks are launched, to help it make locality-aware task-core mapping decisions. On the other side, instead of configuring number of threads explicitly, programmers control that by policies defined in NestedMP.We have written a few benchmarks by NestedMP, which shows NestedMP makes the code more concise on most cases. We have implemented NestedMP in GCC 4.8.2 and tested the performance of these benchmarks on a 4-way 8-core SandyBridge server. The result shows NestedMP improves the performance significantly over GCC's OpenMP implementation.

show abstract

“…Schmidl et al create a distance matrix [32] to represent distances between cores and use topology-aware thread binding [33] of parallel loop nests. Maranthe and Mueller automate profiling to guide page placement [23].…”

Section: Related Workmentioning

confidence: 99%

Characterizing and Mitigating Work Time Inflation in Task Parallel Programs

Olivier

Supinski

Schulz

et al. 2013

Scientific Programming

View full text Add to dashboard Cite

Abstract. Task parallelism raises the level of abstraction in shared memory parallel programming to simplify the development of complex applications. However, task parallel applications can exhibit poor performance due to thread idleness, scheduling overheads, and work time inflation -additional time spent by threads in a multithreaded computation beyond the time required to perform the same work in a sequential computation. We identify the contributions of each factor to lost efficiency in various task parallel OpenMP applications and diagnose the causes of work time inflation in those applications.Increased data access latency can cause significant work time inflation in NUMA systems. Our locality framework for task parallel OpenMP programs mitigates this cause of work time inflation. Our extensions to the Qthreads library demonstrate that locality-aware scheduling can improve performance up to 3X compared to the Intel OpenMP task scheduler.

show abstract

Binding Nested OpenMP Programs on Hierarchical Memory Architectures

Cited by 6 publications

References 7 publications

A Performance Study of a Dual Xeon-Phi Cluster for the Forward Modelling of Gravitational Fields

A Performance Study of a Dual Xeon-Phi Cluster for the Forward Modelling of Gravitational Fields

NestedMP: Taming Complex Configuration Space of Degree of Parallelism for Nested-Parallel Programs

Characterizing and Mitigating Work Time Inflation in Task Parallel Programs

Contact Info

Product

Resources

About