Session
FOSDEM 2021 Schedule
HPC, Big Data and Data Science

DiscoPoP: A tool to identify parallelization opportunities in sequential programs and suggest OpenMP constructs and clauses

D.hpc
Mohammad Norouzi
This talk introduces DiscoPoP, a tool which identifies parallelization opportunities in sequential programs and suggests programmers how to parallelize them using OpenMP. The tool first identifies computational units which, in our terminology, are the atoms of parallelization. Then, it profiles memory accesses inside the source code to detect data dependencies. Mapping dependencies to CUs, we create a data structure which we call the program execution tree (PET). Further, DiscoPoP inspects the pet of a program to find parallel design patterns and parallelization suggestions in terms of OpenMP constructs and clauses. By far, DiscoPoP detects doall, reduction, pipeline, task parallelism, and geometric decomposition in a program. We used DiscoPoP to create OpenMP versions of 49 sequential benchmarks and compared them with the code produced by three state-of-the-art parallelization tools: Our codes are faster in most cases with average speedups relative to any of the three ranging from 1.8 to 2.7. Moreover, we analyzed the LULESH program and an astrophysics simulation code with DiscoPoP. In LULESH, we identify most of the parallelization opportunities which have been parallelized by expert programmers manually. In the astrophysics code, DiscoPoP finds unexploited parallelism opportunities and achieves a speed-up of up to 35%. DiscoPoP is released as an open source software and can be downloaded from: https://github.com/discopop-project/discopop

Additional information

Type devroom

More sessions

2/6/21
HPC, Big Data and Data Science
Ali Hajiabadi
D.hpc
With the end of Moore’s law, improving single-core processor performance can be extremely difficult to do in an energy-efficient manner. One alternative is to rethink conventional processor design methodologies and propose innovative ideas to unlock additional performance and efficiency. In an attempt to overcome these difficulties, we propose a compiler-informed non-speculative out-of-order commit processor, that attacks the limitations of in-order commit in current out-of-order cores to ...
2/6/21
HPC, Big Data and Data Science
Christian Kniep
D.hpc
The Container ecosystem spans from spawning a process into an isolated and constrained region of the kernel at bottom layer, building and distributing images just above to discussions on how to schedule a fleet of containers around the world at the very top. While the top layers get all the attention and buzz, this session will base-line the audiences' understanding of how to execute containers.
2/6/21
HPC, Big Data and Data Science
Nicolas Poggi
D.hpc
Over the years, there has been extensive and continuous effort on improving Spark SQL's query optimizer and planner, in order to generate high quality query execution plans. One of the biggest improvements is the cost-based optimization framework that collects and leverages a variety of data statistics (e.g., row count, number of distinct values, NULL values, max/min values, etc.) to help Spark make better decisions in picking the most optimal query plan.
2/6/21
HPC, Big Data and Data Science
Alaina Edwards
D.hpc
In this talk we explore two programming models for GPU accelerated computing in a Fortran application: OpenMP with target directives and CUDA. We use an example application Riemann problem, a common problem in fluid dynamics, as our testing ground. This example application is implemented in GenASiS, a code being developed for astrophysics simulations. While OpenMP and CUDA are supported on the Summit supercomputer, its successor, an exascale supercomputer Frontier, will support OpenMP and ...
2/6/21
HPC, Big Data and Data Science
Bob Dröge
D.hpc
The European Environment for Scientific Software Installations (EESSI, pronounced as “easy”) is a collaboration between different HPC sites and industry partners, with the common goal to set up a shared repository of scientific software installations that can be used on a variety of systems, regardless of which flavor/version of Linux distribution or processor architecture is used, or whether it is a full-size HPC cluster, a cloud environment or a personal workstation. The EESSI codebase ...
2/6/21
HPC, Big Data and Data Science
Robert McLay
D.hpc
XALT is a tool run on clusters to find out what programs and libraries are run. XALT uses the environment variable LD_PRELOAD to attach a shared library to execute code before and after main(). This means that the XALT shared library is a developer on every program run under linux. This shared library is part of every program run. This talk will discuss the various lessons about routine names and memory usage. Adding XALT to track container usage presents new issues because of what shared ...
2/6/21
HPC, Big Data and Data Science
Carsten Kutzner
D.hpc
In this sessions we are presenting several approaches to migrate from traditional HPC to cloud-native, containerized HPC using an ensemble run of the molecular dynamics code GROMACS as an example. The session will show how containerization via software management is coming to the rescue and how a palatable journey might look like.