AllScale: Enriching modern C++ for the efficient development
of HPC applications
By Thomas
Fahringer, Institute of Computer Science, University of Innsbruck,
Austria
Abstract:
The tremendous challenge
of developing applications efficiently, utilizing the hardware provided by
contemporary parallel systems of all scales is among the most limiting factors
for the continuous growth of high-performance computing. In this talk, we
present a novel architecture taking on this challenge by providing an
infrastructure for the effective development of such applications. Our design
combines the expressive power of modern C++, advanced compiler technology, and
sophisticated runtime system solutions, with the goal of providing a clean
separation of domain specific algorithms, resource management activities, and
low-level hardware interactions. The talk covers the architecture design, its
key aspects, and a preliminary evaluation of the achievable performance of an
application implemented based on the proposed infrastructure.
Short Bio:
Thomas Fahringer is
a Professor of Computer Science at the University of Innsbruck. He is leading a
research group in the area of distributed and parallel processing which develops
the ASKALON system to support researchers worldwide in various fields of
science and engineering to develop, analyse, optimize
and run parallel and distributed scientific applications. Furthermore, he leads
a research team that created the Insieme parallelizing
and optimizing compiler for heterogeneous multicore parallel computers.
Fahringer was
involved in numerous national and international research projects including 10
EU funded projects. He currently coordinates two H2020 projects which includes AllScale - an exascale
programming, multi-objective optimization and resilience management environment
based on nested recursive parallelism, 2015 - 2018, and the ENTICE project -
Decentralized repositories for transparent and efficient virtual machine operations,
2015 - 2018. Fahringer has published 5 books, 35 journal and magazine articles
and more than 200 reviewed conference papers including 4 best/distinguished
IEEE/ACM papers.
email: tf@dps.uibk.ac.at
URL: http://www.dps.uibk.ac.at
The ANTAREX
HPC Approach to
Help Developers on Program Analysis, Parallelization and Runtime Autotuning
By João MP Cardoso, Faculty of Engineering,
University of Porto, Porto, Portugal
Abstract:
Developing and optimizing applications for HPC systems
and considering energy-efficiency is an extremely challenging problem. They are
difficult and complex tasks that require mastering programming languages, tools
for performance tuning, and the target HPC systems. In the ANTAREX project, a
DSL-based approach allows developers to apply strategies (recipes) to
applications regarding extra-functional requirements, such as performance and
energy-efficiency. We provide a holistic and versatile approach spanning
various decision layers composing the supercomputer software stack and with the
advantage to exploit effectively system capabilities. In this talk, we present
the ANTAREX toolflow to enable the definition of
energy-efficiency, performance, and adaptivity
strategies as well as their enforcement at runtime through application autotuning. We show how the ANTAREX toolflow
can effectively assist various development/ optimization stages, including application
analysis and profiling, code transformations and parallelization, and
integration of runtime autotuning.
Short Bio:
João M. P. Cardoso got his PhD degree in Electrical
and Computer Engineering from the IST/UTL (Technical University of Lisbon),
Lisbon, Portugal in 2001. He is Full Professor at the Dep. of Informatics Eng.,
Faculty of Eng. of the University of Porto, Porto, Portugal, and a research
member of INESC TEC. Before, he was with the IST/UTL (2006-2008), a senior
researcher at INESC-ID (2001-2009), and with the University of Algarve
(1993-2006). In 2001/2002, he worked for PACT XPP Technologies, Inc., Munich,
Germany. He has been involved in the
organization and served as a Program Committee member for many Int’l
Conferences. He was General Co-Chair of IEEE/IFIP EUC’2015 and IEEE CSE’2015,
General Chair of FPL’2013, General Co-Chair of ARC’2014 and ARC’2006, Program
Co-Chair of ARCS’2016, DASIP’2014, and RAW’2010. He is co-author of two books
(Elsevier and Springer), co-editor of two Springer Books and four Springer LNCS
volumes. He has (co-)authored over 200 scientific publications (including
journal/conference papers and patents) on subjects related to compilers,
embedded systems, and reconfigurable computing. He has participated in a number
of international research projects, e.g., as co-scientific coordinator of the
FP7 EU-funded project REFLECT (2010-2012), and as coordinator of a number of
national funded projects. He is a senior member of IEEE, a member of IEEE
Computer Society, and a senior member of ACM.
His research interests include compilation techniques, domain-specific
languages, reconfigurable computing, high-level synthesis and
application-specific architectures, and high-performance computing with a
particular emphasis in embedded computing.
READEX: Runtime Exploitation of Application Dynamism for Energy-efficient eXascale computing
by Umbreen
Sabir Mian,
Technical University Dresden (TUD), Dresden, Germany
Abstract:
The goal of the
READEX project is to improve energy-efficiency of applications in the field of
High-Performance Computing. The project brings together European experts from different
ends of the computing spectrum to develop a tools-aided methodology for dynamic
auto-tuning, allowing users to automatically exploit the dynamic behaviour of their applications by adjusting the system to
the actual resource requirements.
In our talk, we will
present the latest advances and developments of the project.
Short Bio:
Umbreen Sabir Mian
is a doctoral researcher at Technical University Dresden (TUD) where she is
working on a project which focuses on dynamic energy efficiency tuning for exascale computing. Her area of research includes HPC
performance analysis and energy efficiency tuning for HPC applications. She
received her Bachelors and Masters in Computer Engineering from University of
Engineering and Technology, Taxila (UETT). After serving for three years at UETT as
lecturer, she moved to Munich to pursue her research career. She did her
Masters in Computer Science from Technical University Munich with
specialization in Computer Architecture and HPC
The
ExCAPE Project: Machine Learning on HPC
by Thomas Ashby, IMEC, Belgium,
and Jan Martinovic, IT4Innovations, Czech Republic
Abstract:
The partners in the ExCAPE project are developing algorithms for large scale machine
learning, studying how those algorithms interact with HPC machines, developing
supporting software, and running tests with the prototypes. The use-case
driving the technical work is the building of multi-task machine learning
models to predict compound-target activity for small molecule bioassays used in
pharmaceutical drug discovery. In this talk we will give a summary of the main
work and results produced so far in the project, and a more detailed overview
of the software prototypes produced with the aim of fostering interactions with
HPC researchers outside of the project. We will also touch on other use-cases
for machine learning at scale.
Short Bios:
Dr. Ashby received
his PhD from the University of Edinburgh (UK) on computational
and computer science, focusing on the programmability and performance of
computational solvers for scientific computing, incorporating algorithmic
analysis, use of high level languages and compiler optimisations.
After a brief stint doing computer architecture, he joined Imec, Leuven,
Belgium in 2007 and has worked on embedded systems, parallel programming tools,
HPC and machine learning. His research interests include numerical algorithms,
software engineering for computational problems, computer system architecture
and tools, and performance engineering.
Dr. Jan Martinovic
is currently Head of Advanced Data Analysis and Simulations Lab at
IT4Innovations National Supercomputing Centre of the Czech Republic, located at
the VSB - Technical University of Ostrava. His research is focused on large
scale data processing and analysis as well as on data based simulations with
diverse real-life applications. Those applications comprise support for
management and decision-making in emergency situations, intelligent navigation
and traffic prediction, flood modelling, smart cities and information
retrieval. It also includes research work on HyperLoom
which is a platform for defining and executing workflow pipelines in
large-scale distributed environments (http://HyperLoom.eu). His activities also
cover a development of High-End Application Execution Middleware which allows
using HPC infrastructure remotely by specific API (http://HEAppE.eu).