2,202 research outputs found
DISPATCH: A Numerical Simulation Framework for the Exa-scale Era. I. Fundamentals
We introduce a high-performance simulation framework that permits the
semi-independent, task-based solution of sets of partial differential
equations, typically manifesting as updates to a collection of `patches' in
space-time. A hybrid MPI/OpenMP execution model is adopted, where work tasks
are controlled by a rank-local `dispatcher' which selects, from a set of tasks
generally much larger than the number of physical cores (or hardware threads),
tasks that are ready for updating. The definition of a task can vary, for
example, with some solving the equations of ideal magnetohydrodynamics (MHD),
others non-ideal MHD, radiative transfer, or particle motion, and yet others
applying particle-in-cell (PIC) methods. Tasks do not have to be grid-based,
while tasks that are, may use either Cartesian or orthogonal curvilinear
meshes. Patches may be stationary or moving. Mesh refinement can be static or
dynamic. A feature of decisive importance for the overall performance of the
framework is that time steps are determined and applied locally; this allows
potentially large reductions in the total number of updates required in cases
when the signal speed varies greatly across the computational domain, and
therefore a corresponding reduction in computing time. Another feature is a
load balancing algorithm that operates `locally' and aims to simultaneously
minimise load and communication imbalance. The framework generally relies on
already existing solvers, whose performance is augmented when run under the
framework, due to more efficient cache usage, vectorisation, local
time-stepping, plus near-linear and, in principle, unlimited OpenMP and MPI
scaling.Comment: 17 pages, 8 figures. Accepted by MNRA
The role of graphics super-workstations in a supercomputing environment
A new class of very powerful workstations has recently become available which integrate near supercomputer computational performance with very powerful and high quality graphics capability. These graphics super-workstations are expected to play an increasingly important role in providing an enhanced environment for supercomputer users. Their potential uses include: off-loading the supercomputer (by serving as stand-alone processors, by post-processing of the output of supercomputer calculations, and by distributed or shared processing), scientific visualization (understanding of results, communication of results), and by real time interaction with the supercomputer (to steer an iterative computation, to abort a bad run, or to explore and develop new algorithms)
Parallel symbolic state-space exploration is difficult, but what is the alternative?
State-space exploration is an essential step in many modeling and analysis
problems. Its goal is to find the states reachable from the initial state of a
discrete-state model described. The state space can used to answer important
questions, e.g., "Is there a dead state?" and "Can N become negative?", or as a
starting point for sophisticated investigations expressed in temporal logic.
Unfortunately, the state space is often so large that ordinary explicit data
structures and sequential algorithms cannot cope, prompting the exploration of
(1) parallel approaches using multiple processors, from simple workstation
networks to shared-memory supercomputers, to satisfy large memory and runtime
requirements and (2) symbolic approaches using decision diagrams to encode the
large structured sets and relations manipulated during state-space generation.
Both approaches have merits and limitations. Parallel explicit state-space
generation is challenging, but almost linear speedup can be achieved; however,
the analysis is ultimately limited by the memory and processors available.
Symbolic methods are a heuristic that can efficiently encode many, but not all,
functions over a structured and exponentially large domain; here the pitfalls
are subtler: their performance varies widely depending on the class of decision
diagram chosen, the state variable order, and obscure algorithmic parameters.
As symbolic approaches are often much more efficient than explicit ones for
many practical models, we argue for the need to parallelize symbolic
state-space generation algorithms, so that we can realize the advantage of both
approaches. This is a challenging endeavor, as the most efficient symbolic
algorithm, Saturation, is inherently sequential. We conclude by discussing
challenges, efforts, and promising directions toward this goal
Feedback and time are essential for the optimal control of computing systems
The performance, reliability, cost, size and energy usage of computing systems can be improved by one or more orders of magnitude by the systematic use of modern control and optimization methods. Computing systems rely on the use of feedback algorithms to schedule tasks, data and resources, but the models that are used to design these algorithms are validated using open-loop metrics. By using closed-loop metrics instead, such as the gap metric developed in the control community, it should be possible to develop improved scheduling algorithms and computing systems that have not been over-engineered. Furthermore, scheduling problems are most naturally formulated as constraint satisfaction or mathematical optimization problems, but these are seldom implemented using state of the art numerical methods, nor do they explicitly take into account the fact that the scheduling problem itself takes time to solve. This paper makes the case that recent results in real-time model predictive control, where optimization problems are solved in order to control a process that evolves in time, are likely to form the basis of scheduling algorithms of the future. We therefore outline some of the research problems and opportunities that could arise by explicitly considering feedback and time when designing optimal scheduling algorithms for computing systems
Parallelising multi-agent systems for high performance computing
Multi-Agent Systems (MAS) are seen as a promising technology to face the current requirements of large-scale distributed and complex systems, e.g., autonomous traffic systems or risk management. The application of MAS to such large scale systems, characterised by millions of distributed nodes, imposes special demanding requirements in terms of
fast computation. The paper discusses the parallelisation of MAS solutions using larger-scale distributed High End
Computing platforms as well as High Performance Computing as a suitable approach to handle the complexity associated to
collaborative solutions for large-scale systems
Software for Exascale Computing - SPPEXA 2016-2019
This open access book summarizes the research done and results obtained in the second funding phase of the Priority Program 1648 "Software for Exascale Computing" (SPPEXA) of the German Research Foundation (DFG) presented at the SPPEXA Symposium in Dresden during October 21-23, 2019. In that respect, it both represents a continuation of Vol. 113 in Springer’s series Lecture Notes in Computational Science and Engineering, the corresponding report of SPPEXA’s first funding phase, and provides an overview of SPPEXA’s contributions towards exascale computing in today's sumpercomputer technology. The individual chapters address one or more of the research directions (1) computational algorithms, (2) system software, (3) application software, (4) data management and exploration, (5) programming, and (6) software tools. The book has an interdisciplinary appeal: scholars from computational sub-fields in computer science, mathematics, physics, or engineering will find it of particular interest
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