OpenMP: Heterogenous Execution and Data Movements: 11th by Christian Terboven, Bronis R. de Supinski, Pablo Reble,

By Christian Terboven, Bronis R. de Supinski, Pablo Reble, Barbara M. Chapman, Matthias S. Müller

This e-book constitutes the refereed lawsuits of the eleventh foreign Workshop on OpenMP, held in Aachen, Germany, in October 2015.

The 19 technical complete papers provided have been conscientiously reviewed and chosen from 22 submissions. The papers are equipped in topical sections on purposes, accelerator purposes, instruments, extensions, compiler and runtime, and energy.

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Additional resources for OpenMP: Heterogenous Execution and Data Movements: 11th International Workshop on OpenMP, IWOMP 2015, Aachen, Germany, October 1–2, 2015, Proceedings

Example text

Heavyweight cores provide maximum compute performance in a single node using 16 or more cores clocked at a high rate, sharing coherent, high-bandwidth memory. They are the easiest to program and suitable for a large number of applications. In contrast, lightweight nodes use simpler cores with lower-bandwidth memory that consume less power. As a consequence, they are better able to scale to thousands of nodes in a system. Figure 1 shows that the best heavyweight single-node implementation is equivalent in performance to a large multiple of lightweight nodes.

Teams creates a league of thread teams, and the master thread of each team executes the region. distribute is closely nested in a teams region to share work among master threads of teams. Other features in the OpenMP accelerator model include a target update directive to make specified items in the device data environment consistent with their original list items, a target declare directive to specify the variables or functions to be mapped to a device, some combined constructs to simplify the programming, and an environment variable (OMP DEFAULT DEVICE) to indicate the default device number, and a set of runtime library routines to set and detect information related to accelerators.

Section 5 describes the implementation of our model in the LLVM compiler and we present preliminary results in Sect. 6. Section 7 discusses extensions to the model and implementation that we plan to explore in the future before we conclude in Sect. 8. 1 and prior versions on distributed machines by translating shared-memory programs onto a Software Distributed Shared Memory (SDSM) runtime [7,13] or directly to MPI [11]. An SDSM runtime that transparently keeps memory consistent between nodes was first used in TreadMarks [7] to execute OpenMP programs and was later incorporated into Intel’s Cluster OMP [6].

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