New subject: [Hpcstaff] [Informatik-assistenten] Einladung: Informatik-Oberseminar Joachim Protze

12 Jul 2021

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Zeit:  Donnerstag, 22. Juli 2002, 13.00 Uhr

Ort:   Zoom Videokonferenz
https://rwth.zoom.us/j/97475619537?pwd=NzBLYkFqREVISyt3QnNSd1ZoK2NZZz09

Referent: Dipl.-Inf. Joachim Protze
          Lehrstuhl Informatik 12

Thema: Modular Techniques and Interfaces for Data Race Detection in
       Multi-Paradigm Parallel Programming

Abstract:

The demand for ever-growing computing capabilities in scientific
computing and simulation has led to heterogeneous computing systems with
multiple parallelism levels. The aggregated performance of the Top 500
high-performance computing (HPC) systems showed an annual growth rate of
85% for the years 1993-2013. As this growth rate significantly exceeds
the growth rate of 40% to 60% supported by Moore’s law, the additional
growth was always supported by an increasing number of computing nodes
with distributed memory and connected by a network. The message passing
interface (MPI) proved to be the dominating programming paradigm for
distributed memory computing as the most coarse-grain level of
parallelism in HPC. While performance gain from Moore’s law in the last
century mainly went into single-core performance by increasing the clock
frequency, we see an increasing number of computing cores per socket
since the beginning of this century. The cores within a socket or a
node share the memory. Although MPI can be used and is used for shared
memory parallelization, explicit use of shared memory as with OpenMP can
improve the scalability and performance of parallel applications. As a
result, hybrid MPI and OpenMP programming is a common paradigm in HPC.

Memory access anomalies such as data races are a severe issue in
parallel programming. Data race detection has been studied for years,
and different static and dynamic analysis techniques have been
presented. This work will not try and propose fundamentally new analysis
techniques but will show how high-level abstraction of MPI and OpenMP
can be mapped to the low-level abstraction of analysis tools without
impact on the analysis’s soundness. This work develops and presents
analysis workflows to identify memory access anomalies in hybrid, multi-
paradigm parallel applications. This work collects parallel variants of
memory access anomalies known from sequential programming and identifies
specific patterns for distributed and shared memory programming.
Furthermore, this work identifies the high-level synchronization,
concurrency, and memory access semantics implicitly and explicitly
defined by the parallel programming paradigms’ specifications to provide
a mapping to the analysis abstraction. As part of these high-level
concurrency concepts, we can identify several sources of concurrency
within a thread. This work compares two techniques to handle this high-
level concurrency for data race analysis and finds that a combined
approach works best in the general case. The evaluation shows that this
work’s analysis workflow provides a high precision while enabling
increased recall for concurrency within a thread.

In this talk, we will focus on the mapping of high-level concurrency
abstractions to low-level analysis abstractions as an important key
point of this thesis and present the results of the work.

Es laden ein: die Dozentinnen und Dozenten der Informatik

Einladung: Informatik-Oberseminar Joachim Protze

Joachim Protze

Joachim Protze

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