82 research outputs found
Optimizing NEURON Simulation Environment Using Remote Memory Access with Recursive Doubling on Distributed Memory Systems
Increase in complexity of neuronal network models escalated the efforts to make NEURON simulation environment efficient. The computational neuroscientists divided the equations into subnets amongst multiple processors for achieving better hardware performance. On parallel machines for neuronal networks, interprocessor spikes exchange consumes large section of overall simulation time. In NEURON for communication between processors Message Passing Interface (MPI) is used. MPI_Allgather collective is exercised for spikes exchange after each interval across distributed memory systems. The increase in number of processors though results in achieving concurrency and better performance but it inversely affects MPI_Allgather which increases communication time between processors. This necessitates improving communication methodology to decrease the spikes exchange time over distributed memory systems. This work has improved MPI_Allgather method using Remote Memory Access (RMA) by moving two-sided communication to one-sided communication, and use of recursive doubling mechanism facilitates achieving efficient communication between the processors in precise steps. This approach enhanced communication concurrency and has improved overall runtime making NEURON more efficient for simulation of large neuronal network models
A Taxonomy of Workflow Management Systems for Grid Computing
With the advent of Grid and application technologies, scientists and
engineers are building more and more complex applications to manage and process
large data sets, and execute scientific experiments on distributed resources.
Such application scenarios require means for composing and executing complex
workflows. Therefore, many efforts have been made towards the development of
workflow management systems for Grid computing. In this paper, we propose a
taxonomy that characterizes and classifies various approaches for building and
executing workflows on Grids. We also survey several representative Grid
workflow systems developed by various projects world-wide to demonstrate the
comprehensiveness of the taxonomy. The taxonomy not only highlights the design
and engineering similarities and differences of state-of-the-art in Grid
workflow systems, but also identifies the areas that need further research.Comment: 29 pages, 15 figure
A Power Efficient Routing algorithm based on betweenness
[[abstract]]In recent years, routing and efficient energy are important topics in mobile ad hoc networks (MANET). According to the resources of MANET are limited, such that, advance routing strategies have to be considered the issues of resources consumption and transmission effect. Complex network has non-trivial topological features. A network can be measured by multiple properties and be presented network behavior, such as betweenness. We propose a new routing algorithm with betweenness analysis. The results show that our algorithm is used to increase network lifetime.[[conferencetype]]ĺś‹éš›[[conferencedate]]20100521~20100524[[iscallforpapers]]Y[[conferencelocation]]Wuhan, Chin
Coordinating Components in the Multimedia System Sercices Architecture
The purpose of this work is to examine and exploit the potential of the coordination paradigm to act as the main communication and synchronization mechanism between components forming a distributed multimedia environment and exhibiting real-time properties. Towards this purpose, we have developed a mechanism for coordinating the distributed execution of components, as these are defined by the Multimedia System Services Architecture (MSSA). Our coordination environment uses the control-driven approach to coordination, namely the model IWIM and the associated language Manifold. In the process we show how Manifold can be used to realize object communication and synchronization of MSSA components and we present a methodology of combining a software architecture such as MSSA with a coordination language such as Manifold. We illustrate our approach by means of a suitable example
Checking Linearizability of Concurrent Priority Queues
Efficient implementations of concurrent objects such as atomic collections
are essential to modern computing. Programming such objects is error prone: in
minimizing the synchronization overhead between concurrent object invocations,
one risks the conformance to sequential specifications -- or in formal terms,
one risks violating linearizability. Unfortunately, verifying linearizability
is undecidable in general, even on classes of implementations where the usual
control-state reachability is decidable. In this work we consider concurrent
priority queues which are fundamental to many multi-threaded applications such
as task scheduling or discrete event simulation, and show that verifying
linearizability of such implementations can be reduced to control-state
reachability. This reduction entails the first decidability results for
verifying concurrent priority queues in the context of an unbounded number of
threads, and it enables the application of existing safety-verification tools
for establishing their correctness.Comment: An extended abstract is published in the Proceedings of CONCUR 201
Run-time optimization of adaptive irregular applications
Compared to traditional compile-time optimization, run-time optimization could offer significant performance improvements when parallelizing and optimizing adaptive irregular applications, because it performs program analysis and adaptive optimizations during program execution. Run-time techniques can succeed where static techniques fail because they exploit the characteristics of input data, programs' dynamic behaviors, and the underneath execution environment. When optimizing adaptive irregular applications for parallel execution, a common observation is that the effectiveness of the optimizing transformations depends on programs' input data and their dynamic phases. This dissertation presents a set of run-time optimization techniques that match the characteristics of programs' dynamic memory access patterns and the appropriate optimization (parallelization) transformations. First, we present a general adaptive algorithm selection framework to automatically and adaptively select at run-time the best performing, functionally equivalent algorithm for each of its execution instances. The selection process is based on off-line automatically generated prediction models and characteristics (collected and analyzed dynamically) of the algorithm's input data, In this dissertation, we specialize this framework for automatic selection of reduction algorithms. In this research, we have identified a small set of machine independent high-level characterization parameters and then we deployed an off-line, systematic experiment process to generate prediction models. These models, in turn, match the parameters to the best optimization transformations for a given machine. The technique has been evaluated thoroughly in terms of applications, platforms, and programs' dynamic behaviors. Specifically, for the reduction algorithm selection, the selected performance is within 2% of optimal performance and on average is 60% better than "Replicated Buffer," the default parallel reduction algorithm specified by OpenMP standard. To reduce the overhead of speculative run-time parallelization, we have developed an adaptive run-time parallelization technique that dynamically chooses effcient shadow structures to record a program's dynamic memory access patterns for parallelization. This technique complements the original speculative run-time parallelization technique, the LRPD test, in parallelizing loops with sparse memory accesses. The techniques presented in this dissertation have been implemented in an optimizing research compiler and can be viewed as effective building blocks for comprehensive run-time optimization systems, e.g., feedback-directed optimization systems and dynamic compilation systems
An FPGA Task Placement Algorithm Using Reflected Binary Gray Space Filling Curve
With the arrival of partial reconfiguration technology, modern FPGAs support tasks that can be loaded in (removed from) the FPGA individually without interrupting other tasks already running on the same FPGA. Many online task placement algorithms designed for such partially reconfigurable systems have been proposed to provide efficient and fast task placement. A new approach for online placement of modules on reconfigurable devices, by managing the free space using a run-length based representation. This representation allows the algorithm to insert or delete tasks quickly and also to calculate the fragmentation easily. In the proposed FPGA model, the CLBs are numbered according to reflected binary gray space filling curve model. The search algorithm will quickly identify a placement for the incoming task based on first fit mode or a fragmentation aware best fit mode. Simulation experiments indicate that the proposed techniques result in a low ratio of task rejection and high FPGA utilization compared to existing techniques
Optimierte Implementierung ausgewählter kollektiver Operationen unter Ausnutzung der Hardwareparallelität des InfiniBand Netzwerkes
Ziel der Arbet ist eine optimierte Implementierung der im MPI-1 Standard definierten Reduktionsoperationen MPI_Reduce(), MPI_Allreduce(), MPI_Scan(), MPI_Reduce_scatter() für das InfiniBand Netzwerk. Hierbei soll besonderer Wert auf spezielle InfiniBand Operationen und die Hardwareparallelität gelegt werden.
InfiniBand ermöglicht es Kommunikationsoperationen klar von Berechnungen zu trennen, was eine Überlappung beider Operationstypen in der Reduktion ermöglicht. Das Potential dieser Methode soll modelltheoretisch als auch praktisch in einer prototypischen Implementierung im Rahmen des Open MPI Frameworks erfolgen. Das Endresultat soll mit vorhandenen Implementierungen (z.B. MVAPICH) verglichen werden.The performance of collective communication operations is one of the deciding factors in the overall performance of a MPI application. Current implementations of MPI use the point-to-point components to access the InfiniBand network. Therefore it is tried to improve the performance of a collective component by accessing the InfiniBand network directly. This should avoid overhead and make it possible to tune the algorithms to this specific network. Various algorithms for the MPI_Reduce, MPI_Allreduce, MPI_Scan and MPI_Reduce_scatter operations are presented. The theoretical performance of the algorithms is analyzed with the LogfP and LogGP models. Selected algorithms are implemented as part of an Open MPI collective component. Finally the performance of different algorithms and different MPI implementations is compared
Run-time optimization of adaptive irregular applications
Compared to traditional compile-time optimization, run-time optimization could offer significant performance improvements when parallelizing and optimizing adaptive irregular applications, because it performs program analysis and adaptive optimizations during program execution. Run-time techniques can succeed where static techniques fail because they exploit the characteristics of input data, programs' dynamic behaviors, and the underneath execution environment. When optimizing adaptive irregular applications for parallel execution, a common observation is that the effectiveness of the optimizing transformations depends on programs' input data and their dynamic phases. This dissertation presents a set of run-time optimization techniques that match the characteristics of programs' dynamic memory access patterns and the appropriate optimization (parallelization) transformations. First, we present a general adaptive algorithm selection framework to automatically and adaptively select at run-time the best performing, functionally equivalent algorithm for each of its execution instances. The selection process is based on off-line automatically generated prediction models and characteristics (collected and analyzed dynamically) of the algorithm's input data, In this dissertation, we specialize this framework for automatic selection of reduction algorithms. In this research, we have identified a small set of machine independent high-level characterization parameters and then we deployed an off-line, systematic experiment process to generate prediction models. These models, in turn, match the parameters to the best optimization transformations for a given machine. The technique has been evaluated thoroughly in terms of applications, platforms, and programs' dynamic behaviors. Specifically, for the reduction algorithm selection, the selected performance is within 2% of optimal performance and on average is 60% better than "Replicated Buffer," the default parallel reduction algorithm specified by OpenMP standard. To reduce the overhead of speculative run-time parallelization, we have developed an adaptive run-time parallelization technique that dynamically chooses effcient shadow structures to record a program's dynamic memory access patterns for parallelization. This technique complements the original speculative run-time parallelization technique, the LRPD test, in parallelizing loops with sparse memory accesses. The techniques presented in this dissertation have been implemented in an optimizing research compiler and can be viewed as effective building blocks for comprehensive run-time optimization systems, e.g., feedback-directed optimization systems and dynamic compilation systems
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