36 research outputs found
Coarray-based Load Balancing on Heterogeneous and Many-Core Architectures
In order to reach challenging performance goals, computer architecture is expected to change significantly in the near future. Heterogeneous chips, equipped with different types of cores and memory, will force application developers to deal with irregular communication patterns, high levels of parallelism, and unexpected behavior.
Load balancing among the heterogeneous compute units will be a critical task in order to achieve an effective usage of the computational power provided by such new architectures. In this highly dynamic scenario, Partitioned Global Address Space (PGAS) languages, like Coarray Fortran, appear a promising alternative to standard MPI programming that uses two-sided communications, in particular because of PGAS one-sided semantic and ease of programmability. In this paper, we show how Coarray Fortran can be used for implementing dynamic load balancing algorithms on an exascale compute node and how these algorithms can produce performance benefits for an Asian option pricing problem, running in symmetric mode on Intel Xeon Phi Knights Corner and Knights Landing architectures
A framework for unit testing with coarray Fortran
Parallelism is a ubiquitous feature of modern computing architectures; indeed, we might even say that serial code is now automatically legacy code. Writing parallel code poses significant challenges to programs, and is often error-prone. Partitioned Global Address Space (PGAS) languages, such as Coarray Fortran (CAF), represent a promising development direction in the quest for a trade-off between simplicity and performance. CAF is a parallel programming model that allows a smooth migration from serial to parallel code. However, despite CAF simplicity, refactoring serial code and migrating it to parallel versions is still error-prone, especially in complex softwares. The combination of unit testing, which drastically reduces defect injection, and CAF is therefore a very appealing prospect; however, it requires appropriate tools to realize its potential. In this paper, we present the first CAF-compatible framework for unit tests, developed as an extension to the Parallel Fortran Unit Test framework (pFUnit)
X10 for high-performance scientific computing
High performance computing is a key technology that enables large-scale physical
simulation in modern science. While great advances have been made in methods and
algorithms for scientific computing, the most commonly used programming models
encourage a fragmented view of computation that maps poorly to the underlying
computer architecture.
Scientific applications typically manifest physical locality, which means that interactions
between entities or events that are nearby in space or time are stronger
than more distant interactions. Linear-scaling methods exploit physical locality by approximating
distant interactions, to reduce computational complexity so that cost is
proportional to system size. In these methods, the computation required for each
portion of the system is different depending on that portion’s contribution to the
overall result. To support productive development, application programmers need
programming models that cleanly map aspects of the physical system being simulated
to the underlying computer architecture while also supporting the irregular
workloads that arise from the fragmentation of a physical system.
X10 is a new programming language for high-performance computing that uses
the asynchronous partitioned global address space (APGAS) model, which combines
explicit representation of locality with asynchronous task parallelism. This thesis
argues that the X10 language is well suited to expressing the algorithmic properties
of locality and irregular parallelism that are common to many methods for physical
simulation.
The work reported in this thesis was part of a co-design effort involving researchers
at IBM and ANU in which two significant computational chemistry codes
were developed in X10, with an aim to improve the expressiveness and performance
of the language. The first is a Hartree–Fock electronic structure code, implemented
using the novel Resolution of the Coulomb Operator approach. The second evaluates
electrostatic interactions between point charges, using either the smooth particle
mesh Ewald method or the fast multipole method, with the latter used to simulate
ion interactions in a Fourier Transform Ion Cyclotron Resonance mass spectrometer.
We compare the performance of both X10 applications to state-of-the-art software
packages written in other languages.
This thesis presents improvements to the X10 language and runtime libraries for
managing and visualizing the data locality of parallel tasks, communication using
active messages, and efficient implementation of distributed arrays. We evaluate these improvements in the context of computational chemistry application examples.
This work demonstrates that X10 can achieve performance comparable to established
programming languages when running on a single core. More importantly,
X10 programs can achieve high parallel efficiency on a multithreaded architecture,
given a divide-and-conquer pattern parallel tasks and appropriate use of worker-local
data. For distributed memory architectures, X10 supports the use of active messages
to construct local, asynchronous communication patterns which outperform global,
synchronous patterns. Although point-to-point active messages may be implemented
efficiently, productive application development also requires collective communications;
more work is required to integrate both forms of communication in the X10
language. The exploitation of locality is the key insight in both linear-scaling methods and
the APGAS programming model; their combination represents an attractive opportunity
for future co-design efforts
The Scalability-Efficiency/Maintainability-Portability Trade-off in Simulation Software Engineering: Examples and a Preliminary Systematic Literature Review
Large-scale simulations play a central role in science and the industry.
Several challenges occur when building simulation software, because simulations
require complex software developed in a dynamic construction process. That is
why simulation software engineering (SSE) is emerging lately as a research
focus. The dichotomous trade-off between scalability and efficiency (SE) on the
one hand and maintainability and portability (MP) on the other hand is one of
the core challenges. We report on the SE/MP trade-off in the context of an
ongoing systematic literature review (SLR). After characterizing the issue of
the SE/MP trade-off using two examples from our own research, we (1) review the
33 identified articles that assess the trade-off, (2) summarize the proposed
solutions for the trade-off, and (3) discuss the findings for SSE and future
work. Overall, we see evidence for the SE/MP trade-off and first solution
approaches. However, a strong empirical foundation has yet to be established;
general quantitative metrics and methods supporting software developers in
addressing the trade-off have to be developed. We foresee considerable future
work in SSE across scientific communities.Comment: 9 pages, 2 figures. Accepted for presentation at the Fourth
International Workshop on Software Engineering for High Performance Computing
in Computational Science and Engineering (SEHPCCSE 2016
XcalableMP PGAS Programming Language
XcalableMP is a directive-based parallel programming language based on Fortran and C, supporting a Partitioned Global Address Space (PGAS) model for distributed memory parallel systems. This open access book presents XcalableMP language from its programming model and basic concept to the experience and performance of applications described in XcalableMP.  XcalableMP was taken as a parallel programming language project in the FLAGSHIP 2020 project, which was to develop the Japanese flagship supercomputer, Fugaku, for improving the productivity of parallel programing. XcalableMP is now available on Fugaku and its performance is enhanced by the Fugaku interconnect, Tofu-D. The global-view programming model of XcalableMP, inherited from High-Performance Fortran (HPF), provides an easy and useful solution to parallelize data-parallel programs with directives for distributed global array and work distribution and shadow communication. The local-view programming adopts coarray notation from Coarray Fortran (CAF) to describe explicit communication in a PGAS model. The language specification was designed and proposed by the XcalableMP Specification Working Group organized in the PC Consortium, Japan. The Omni XcalableMP compiler is a production-level reference implementation of XcalableMP compiler for C and Fortran 2008, developed by RIKEN CCS and the University of Tsukuba. The performance of the XcalableMP program was used in the Fugaku as well as the K computer. A performance study showed that XcalableMP enables a scalable performance comparable to the message passing interface (MPI) version with a clean and easy-to-understand programming style requiring little effort