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Geimer, Markus; Wolf, Felix; Wylie, Brian J. N.; Ábrahám, Erika; Becker, Daniel; Mohr, Bernd

doi:10.1002/cpe.v22:6

Cited by 174 publications

(22 citation statements)

References 70 publications

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“…Several performance tools use measurement for the purposes of offline performance analysis, including TAU [32], HPCToolkit [1], Scalasca [38], Vampir [20], Extrae [27] and others. All are powerful and capable tools in their own right.…”

Section: Related Workmentioning

confidence: 99%

An Autonomic Performance Environment for Exascale

Huck

Porterfield²,

Chaimov

et al. 2015

JSFI

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Exascale systems will require new approaches to performance observation, analysis, and runtime decision-making to optimize for performance and efficiency. The standard "first-person" model, in which multiple operating system processes and threads observe themselves and record first-person performance profiles or traces for offline analysis, is not adequate to observe and capture interactions at shared resources in highly concurrent, dynamic systems. Further, it does not support mechanisms for runtime adaptation. Our approach, called APEX (Autonomic Performance Environment for eXascale), provides mechanisms for sharing information among the layers of the software stack, including hardware, operating and runtime systems, and application code, both new and legacy. The performance measurement components share information across layers, merging first-person data sets with information collected by third-person tools observing shared hardware and software states at node-and global-levels. Critically, APEX provides a policy engine designed to guide runtime adaptation mechanisms to make algorithmic changes, re-allocate resources, or change scheduling rules when appropriate conditions occur.

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Section: Related Workmentioning

confidence: 99%

An Autonomic Performance Environment for Exascale

Huck

Porterfield²,

Chaimov

et al. 2015

JSFI

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“…Numbers such as the branch prediction ratio or the L1 cache miss rate are of little help in finding sources of inefficiency. The best modern tools will either attribute such numbers to high-level code items, such as source lines or variables [1], or plot timelines [14]. This will show the user where inefficiencies are occurring but will not provide enough information to clearly identify the issue.…”

Section: Examplesmentioning

confidence: 99%

“…Popular existing tools, such as HPCToolkit [1], are certainly helpful for finding where possible bottlenecks might be, even down to the source line, or with data-centric tools [2], down to a variable. However, knowing that execution time, cache misses, or branch mispredictions are associated with a particular line of code or variable is not enough to identify many issues.…”

Section: Introductionmentioning

confidence: 99%

Discovering Barriers to Efficient Execution, Both Obvious and Subtle, Using Instruction-Level Visualization

Koppelman

Michael

2014

2014 First Workshop on Visual Performance Analysis

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Abstract-CPU performance is determined by the interaction between available resources, microarchitectural features, the execution of instructions, and by the data. These elements can interact in complex ways, making it difficult for those seeing only aggregate performance numbers, such as miss ratios and issue rates, to determine whether there are reasonable avenues for performance improvement. A technique called instructionlevel visualization helps users connect these disparate elements by showing the timing of the execution of individual program instructions. The PSE visualization program enhances instructionlevel visualization by showing which instructions contribute to execution inefficiency in a way that makes it easy to locate dependent instructions and the history of events affecting the instruction. A simple annotation system makes it easy for a user to attach custom information. PSE has been used for microarchitecture research, simulator debugging, and for instructional use.

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“…By checkpointing a program's state at regular intervals, the amount of lost computation is limited to the interval from the last checkpoint to the time of crash. There are several functional tools and libraries of checkpointing in the literature, such as CryoPID [55], Distributed MultiThreaded CheckPointing (DMTCP) [16], ComPiler for Portable Checkpointing (CPPC) [163] or OpenVZ [153]. Berkeley Lab Checkpoint/Restart (BLCR) [65], a hybrid kernel/user implementation that provides a robust, production quality implementation that checkpoints applications without requiring changes in the their code, is currently one of the most used tools of checkpointing.…”

Section: Summary Of the State Of Artmentioning

confidence: 99%

“…User space checkpointing packages are highly 82 portable and can typically be compiled and run on any modern Unix. Examples of tools that implement this type of approach are CryoPID [55], that allows the user to capture the state of a running process in Linux and save it to a file, DMTCP [16], a tool to transparently checkpoint the state of multi-threaded and distributed applications, or CPPC [163], a checkpointing tool focused on the insertion of fault tolerance into long-running message-passing applications. HTCondor [91] offers user-level checkpointing and other tools for batch job scheduling.…”

Section: Application Migrationmentioning

confidence: 99%

High Performance Scientific Computing over Hybrid Cloud Platforms

Arroyo¹

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"Croyez ceux qui cherchent la vérité, doutez de ceux qui la trouvent." "Cree a aquellos que buscan la verdad. Duda de los que la han encontrado" André Gide (1952).iii AbstractScientific applications generally require large computational requirements, memory and data management for their execution. Such applications have traditionally used high-performance resources, such as shared memory supercomputers, clusters of PCs with distributed memory, or resources from Grid infrastructures on which the application needs to be adapted to run successfully. In recent years, the advent of virtualization techniques, together with the emergence of Cloud Computing, has caused a major shift in the way these applications are executed. However, the execution management of scientific applications on high performance elastic platforms is not a trivial task.

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Untitled

Cited by 174 publications

References 70 publications

An Autonomic Performance Environment for Exascale

An Autonomic Performance Environment for Exascale

Discovering Barriers to Efficient Execution, Both Obvious and Subtle, Using Instruction-Level Visualization

High Performance Scientific Computing over Hybrid Cloud Platforms

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