Comprehensive Performance Tracking with Vampir 7

Brunst, Holger; Hackenberg, Daniel; Juckeland, Guido; Rohling, H.

doi:10.1007/978-3-642-11261-4_2

Cited by 27 publications

(19 citation statements)

References 5 publications

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“…Using the tracing system VAMPIR [2,3,14,25,26] we can visualize the structure of the algorithm described above. For an example with 8 processors, the results are depicted in Figures 1 and 2.…”

Section: Parallelization Of the Fractional Adams Methodsmentioning

confidence: 99%

An efficient parallel algorithm for the numerical solution of fractional differential equations

Diethelm

2011

fcaa

265

305

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The numerical solution of differential equations of fractional order is known to be a computationally very expensive problem due to the nonlocal nature of the fractional differential operators. We demonstrate that parallelization may be used to overcome these difficulties. To this end we propose to implement the fractional version of the second-order AdamsBashforth-Moulton method on a parallel computer. According to many recent publications, this algorithm has been successfully applied to a large number of fractional differential equations arising from a variety of application areas. The precise nature of the parallelization concept is discussed in detail and some examples are given to show the viability of our approach.MSC 2010 : Primary 65Y05; Secondary 65L05, 65R20

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Section: Parallelization Of the Fractional Adams Methodsmentioning

confidence: 99%

An efficient parallel algorithm for the numerical solution of fractional differential equations

Diethelm

2011

fcaa

265

305

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“…A well established tool set for this purpose consists of the automatic trace analyzer Scalasca (cf. [3] or http://www.scalasca.org), the interactive trace analysis tool Vampir (see [4] or http://www.vampir.eu), the profile analyzer CUBE (cf. [5] or http://www.…”

Section: The Basic Problem and Static Tuning Approaches For Its Solutionmentioning

confidence: 99%

“…The focus of these activities has traditionally been on the software's performance. A well established tool set for this purpose consists of the automatic trace analyzer Scalasca (cf.[3] or http://www.scalasca.org), the interactive trace analysis tool Vampir (see [4] or http://www.vampir.eu), the profile analyzer CUBE (cf.[5] or http://www. scalasca.org/software/cube-4.x), the profiling and tracing system TAU (see [6] or https://www.cs.uoregon.edu/research/tau) and the Periscope Tuning Framework (PTF) (cf.…”

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confidence: 99%

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Tools for assessing and optimizing the energy requirements of high performance scientific computing software

Diethelm¹

2016

Proc Appl Math and Mech

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Score-P is a measurement infrastructure originally designed for the analysis and optimization of the performance of HPC codes. Recent extensions of Score-P and its associated tools now also allow the investigation of energy-related properties and support the user in the implementation of corresponding improvements. Since it would be counterproductive to completely ignore performance issues in this connection, the focus should not be laid exclusively on energy. We therefore aim to optimize software with respect to an objective function that takes into account energy and run time.1 The basic problem and static tuning approaches for its solutionTo satisfy the demands from the scientific computing community, the established high performance computing centers provide a large amount of massively parallel computing hardware. One of the main challenges that the HPC centers have to face today is the cost of the energy required to operate this hardware which already amounts to about 30% of the total cost of ownership of a current HPC system, with a rising tendency [1]. Thus HPC centers will likely force their users to optimize their software with respect to its energy requirements. In this paper, we provide a brief survey of recent developments in this area. An early strategy implemented by certain HPC centers [2] was to set the default CPU clock frequency of their systems to a value much lower than the highest possible frequency. This concept is based on the fact that the total power required by a compute job can be additively decomposed into a static * Corresponding author: e-mail diethelm@gns-mbh.com component P st = const (known as idle power ) and a dynamic part that depends on f and on the voltage U as P dyn ∼ U 2 · f where, to obtain stability of the operation, U needs to be raised when f is increased. The job's total energy is E = T 0 (P st + P dyn )dt. An increase in f decreases the run time T , so the static component of the total energy decreases while the dynamic component may increase. Using a few test runs with typical input data sets, one tries to gain an impression of the nature of this dependence. For a specific example, Fig. 1 shows the result.As expected, a moderate reduction of the clock frequency decreases the energy consumption but also significantly increases the run time. A straightforward application of the idea thus implies that the available hardware cannot be fully utilized, i. e. the number of program runs that can be executed during the system's life cycle is lower than it could be. To justify the hardware investment, it is thus not reasonable to focus only on energy. A more useful metric is the energy delay product EDP = E · T w where E is energy, T is run time, and w is a parameter weighting energy and run time according to the policy of the specific HPC center; typical values are w ∈ {1, 2, 3}. An optimization with respect to such a metric leads to a strategy that permits using higher frequencies in spite of possibly larger energy requirements if the run time savings are sufficien...

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“…Vampir includes an option for applying k-means clustering to processes, choosing k by how many clusters can fit on screen [9]. The clustering is done on events in physical time and messages are omitted.…”

Section: Background and Related Workmentioning

confidence: 99%

Combing the Communication Hairball: Visualizing Parallel Execution Traces using Logical Time

Isaacs

Bremer

Jusufi

et al. 2014

IEEE Trans. Visual. Comput. Graphics

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Fig. 1: Logical timeline and clustered logical timeline views from Ravel, a tool for visualizing parallel execution traces. Events are represented by boxes, colored by their wall-clock delay. The use of logical time reveals communication patterns and leverages developers' understanding of their program's structure. We use the logical time structure to cluster on any metric, which allow us to represent large-scale traces using explorable clusters while still depicting messages with full timelines for a subset of processes.Abstract-With the continuous rise in complexity of modern supercomputers, optimizing the performance of large-scale parallel programs is becoming increasingly challenging. Simultaneously, the growth in scale magnifies the impact of even minor inefficiencies -potentially millions of compute hours and megawatts in power consumption can be wasted on avoidable mistakes or sub-optimal algorithms. This makes performance analysis and optimization critical elements in the software development process. One of the most common forms of performance analysis is to study execution traces, which record a history of per-process events and interprocess messages in a parallel application. Trace visualizations allow users to browse this event history and search for insights into the observed performance behavior. However, current visualizations are difficult to understand even for small process counts and do not scale gracefully beyond a few hundred processes. Organizing events in time leads to a virtually unintelligible conglomerate of interleaved events and moderately high process counts overtax even the largest display. As an alternative, we present a new trace visualization approach based on transforming the event history into logical time inferred directly from happened-before relationships. This emphasizes the code's structural behavior, which is much more familiar to the application developer. The original timing data, or other information, is then encoded through color, leading to a more intuitive visualization. Furthermore, we use the discrete nature of logical timelines to cluster processes according to their local behavior leading to a scalable visualization of even long traces on large process counts. We demonstrate our system using two case studies on large-scale parallel codes.

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Comprehensive Performance Tracking with Vampir 7

Cited by 27 publications

References 5 publications

An efficient parallel algorithm for the numerical solution of fractional differential equations

An efficient parallel algorithm for the numerical solution of fractional differential equations

Tools for assessing and optimizing the energy requirements of high performance scientific computing software

Combing the Communication Hairball: Visualizing Parallel Execution Traces using Logical Time

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