eeClust: Energy-Efficient Cluster Computing

Minartz, Timo; Molka, Daniel; Knobloch, Michael; Krempel, Stephan; Ludwig, Thomas; Nagel, Wolfgang E.; Mohr, Bernd; Falter, Hugo

doi:10.1007/978-3-642-24025-6_10

Cited by 4 publications

(3 citation statements)

References 5 publications

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“…We performed some measurements [16] to show the effect of MPI on the power consumption of the application and visualized them with Vampir [11]. Figure 4 shows a well balanced test-case with two MPI collectives and two calculation phases.…”

Section: Methodsmentioning

confidence: 99%

Determine energy-saving potential in wait-states of large-scale parallel programs

2011

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Energy consumption is one of the major topics in high performance computing (HPC) in the last years. However, little effort is put into energy analysis by developers of HPC applications. We present our approach of combined performance and energy analysis using the performance analysis tool-set Scalasca. Scalascas parallel wait-state analysis is extended by a calculation of the energy-saving potential if a lower power-state can be used.

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Section: Methodsmentioning

confidence: 99%

Determine energy-saving potential in wait-states of large-scale parallel programs

2011

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“…In this section we introduce our local agent which controls the device power modes and listens to the applications resources requirements per node (see figure 3), which has been developed in the eeClust project [22].…”

Section: Hardware Management Daemonmentioning

confidence: 99%

Managing hardware power saving modes for high performance computing

Minartz

Ludwig

Knobloch

et al. 2011

2011 International Green Computing Conference and Workshops

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Energy consumption has become a major topic in high performance computing in the last years. We present an approach to efficiently manage the power states of an poweraware cluster, including the processor, the network cards and the disks.To profit from the lower power consumption of these states we followed the approach to transfer application knowledge (e.g. future hardware use) to a daemon which efficiently manages the hardware device states per cluster node. After introducing our measurement environment we evaluated the general power saving potential of our AMD and Intel computing nodes. Two example high performance applications are showcases for an initial instrumentation which results in a reduction of the Energy-to-Solution between 4 and 8 % with slight increases of the Time-to-Solution.

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“…The approach of the eeClust project ( [3]) is to switch hardware components to a lower power-state in phases where the component is not fully utilized. To identify these phases we extend well-known performance analysis tools which many application developers are familiar with.…”

Section: Project Planmentioning

confidence: 99%

Electronic poster

Knobloch

Minartz

Molka

et al. 2011

Proceedings of the 2011 Companion on High Performance Computing Networking, Storage and Analysis Companion

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Energy-efficiency and power consumption of applications has become a major topic in High Performance Computing in the last years. This poster presents the work of the last two years in the eeClust project 1 . The aim of the project is to reduce the energy consumption of applications on commodity HPC clusters with as little performance impact as possible by an integrated approach of application analysis, efficient management of hardware power-states and monitoring of the clusters power consumption. We outline the overall project plan and present the generation and analysis of traces on application side as well as the hardware management and monitoring on system side in detail. We further introduce eeMark, a benchmark for computational performance and energy efficiency which is especially tailored for HPC systems. Project PlanThe approach of the eeClust project ([3]) is to switch hardware components to a lower power-state in phases where the component is not fully utilized. To identify these phases we extend well-known performance analysis tools which many application developers are familiar with. This keeps the 1 .learning curve relatively flat. We have developed an Application Programming Interface (API) to instrument the application, which communicates the future hardware requirements of the application to a daemon process (called eeDaemon), which then manages the hardware power-states accordingly.To test and validate our approach we procured a powermanageable cluster with 10 nodes (5 nodes Intel Nehalem and 5 nodes AMD Opteron), attached to 3 ZES LMG450 power-meters 2 which allow very accurate measurements at a high frequency. Tracing & AnalysisWe extended two well-known and wildly deployed performance analysis toolsets for trace file analysis. The first is VampirTrace 3 and Vampir 4 from the Center for Information Services and High Performance Computing of TU Dresden, which allow a manual analysis by timeline visualization of program behaviour, e.g. function calls or messages sent, and hardware counter values, together with statistical details of the program execution. The other toolset is Scalasca 5 , developed at Juelich Supercomputing Centre and the German Research School for Simulation Science in Aachen, which performs an automatic trace file analysis to detect patterns that indicate performance problems, especially wait-states, i.e. situations where one process has to wait for one (ore more) another process(es) because of workload imbalances.We developed a VampirTrace Plugin ([5]) to display the power consumption of the cluster nodes as a counter timeline in Vampir. This enables us to correlate the power consumption to program activity and hardware counter values. 2

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eeClust: Energy-Efficient Cluster Computing

Cited by 4 publications

References 5 publications

Determine energy-saving potential in wait-states of large-scale parallel programs

Determine energy-saving potential in wait-states of large-scale parallel programs

Managing hardware power saving modes for high performance computing

Electronic poster

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