A Path To Energy Efficient HPC Datacenters

By Hayk Shoukourian

October 29, 2013

Energy efficiency is rapidly becoming a key factor for many modern high-performance computing (HPC) datacenters. It poses various challenges, which need to be addressed holistically and in an integrated manner, covering the HPC system environment (system hardware and system software), the hosting facility and infrastructure (cooling technologies, energy re-use, power supply chain, etc.), and applications (algorithms, performance metrics, etc.).

Most of the management schemes present in current HPC datacenters do not allow data to be shared between the HPC system environment, hosting facility, and infrastructure. But, it is important to collect and correlate data from all aspects of the datacenter in order to: better understand the interactions between different components of the datacenter; spot the improvement possibilities; and assess any introduced improvements. There are currently no tools that support a complete collection and correlation of energy efficiency relevant data, allowing for a unified view of energy consumption present in the datacenter.

That’s why a new energy measuring and evaluation toolset is being developed at the Leibniz Supercomputing Centre of the Bavarian Academy of Sciences (BAdW-LRZ) which is capable of monitoring and analysing the energy consumption of a supercomputing site in a holistic way, combining the HPC systems with data from the cooling and building infrastructure. The tool, named Power Data Aggregation Monitor (PowerDAM), allows the collection and evaluation of sensor data independently from the source systems and is capable of monitoring not only HPC systems but any other infrastructure that can be represented as a hierarchical tree. It monitors physical sensors as well as virtual sensors which can represent different functional compositions of several physical sensors.

PowerDAM provides a plug-in framework for defining the desired monitored entities such as IT systems, building infrastructure, etc. Two plug-in interfaces for each monitored entity are provided: one for sensor data collection and one for collecting application relevant data (e.g., utilized compute nodes, starting and ending timestamps of application, etc.) from system resource management tools.

PowerDAM is an underlying framework for energy efficiency related research at BAdW-LRZ.

Evaluating and Reporting

Energy-to-Solution (EtS) is an important metric for PowerDAM which denotes the aggregated energy consumption of an application consisting of the energy consumption of utilized compute nodes and partial sub-system components (e.g., system networking and system cooling).

Figure 1 presents the EtS report for an application executed on CoolMUC MPP Linux cluster. The first part of the report (part I) shows the sensor measurements for all utilized components in the order of timestamp, sensor name, value and unit.

Figure 1: EtS Report for an application executed on CoolMUC MPP Linux Cluster

Part II shows all approximations of source measurement data which were considered to be invalid (missing measurements, out of bounds data, etc.). Part III shows the aggregated energy consumption (EtS) of the executed application and provides information on the consumption percentages of computation, networking, and cooling.

The ability to calculate the EtS of an application allows for the further understanding and tuning of the application internally (via change of algorithms, memory access patterns, etc.) as well as externally through hardware adaptation (e.g., static/dynamic voltage frequency scaling).

PowerDAM provides various visualization options such as: the power draw, utilization rate, and averaged CPU temperatures of utilized compute nodes; correlation between power and load for these nodes; different EtS reports; and system power consumption for a given time frame (e.g. day, month, and year). Figure 2 illustrates one of these options – the EtS report (encompassing in parallel to the EtS, the percentages for computation, infrastructure, cooling, and networking) for all executed applications by a given user.

Figure 2: EtS Report for All Jobs Submitted by Given User

PowerDAM “node-map” view displays the dynamic behavior of compute nodes for a given sensor type. This view updates automatically after a customized amount of time and uses a color mapping to classify the behavior of the compute nodes (Figure 3).

Figure 3: Utilization Map of Compute Nodes for CoolMUC Linux Cluster. The color green illustrates the 96% to 100% utilization range. The color white illustrates the 0% and 90% to 95% utilization range. The color red illustrates the 1% to 89% utilization range. (not all compute nodes of the cluster are depicted)

The “node-map” view can be essential for understanding the interconnection between different sensor types. For example, correlating utilization rate (Figure 3) with CPU temperature (Figure 4) allows the investigation of the interdependency between utilization rates and CPU temperatures of defined compute nodes (nodes lxa130 and lxa17).

Figure 4: Temperature Map of Compute Nodes for CoolMUC Linux Cluster (2×8-core AMD CPUs per compute node)
(not all compute nodes of the cluster are depicted)

Further development will allow PowerDAM to: classify applications according to power draw, runtime, performance, and energy consumption; provide data necessary for the enhancement of the resource management systems; and report on datacenter key performance indicators (KPIs) such as PUE, ERE, DCiE, WUE, etc.

More detailed information on PowerDAM is available in the Proceedings of the First International Conference on Information and Communication Technologies for Sustainability under “Towards a Unified Energy Efficiency Evaluation Toolset: An Approach and Its Implementation at Leibniz Supercomputing Centre (LRZ)” and is indexed under DOI 10.3929/ethz-a-007337628.

The development of PowerDAM was made possible by the PRACE Second Implementation Phase project PRACE- 2IP in the Work Package “Prototyping” which has received funding from the European Community’s Seventh Framework Program (FP7/2007-2013) under grant agreement no. RI-283493 and within the SIMOPEK project which has received funding from the German Federal Ministry of Education and Research (BMBF) under grand agreement no. 01IH13007A. The work was achieved using the PRACE Research Infrastructure resources at BAdW-LRZ with support of the State of Bavaria, Germany.

The authors would like to thank Jeanette Wilde for her valuable comments and support.

Author Affiliations

Hayk Shoukourian(1,2); Torsten Wilde(1); Axel Auweter(1); Arndt Bode(1,2)

1Leibniz Supercomputing Centre of the Bavarian Academy of Sciences and Humanities (BAdW-LRZ)

2Technische Universität München (TUM), Fakultät für Informatik

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industy updates delivered to you every week!

TACC Helps ROSIE Bioscience Gateway Expand its Impact

April 26, 2017

Biomolecule structure prediction has long been challenging not least because the relevant software and workflows often require high-end HPC systems that many bioscience researchers lack easy access to. Read more…

By John Russell

Messina Update: The US Path to Exascale in 16 Slides

April 26, 2017

Paul Messina, director of the U.S. Exascale Computing Project, provided a wide-ranging review of ECP’s evolving plans last week at the HPC User Forum. Read more…

By John Russell

IBM, Nvidia, Stone Ridge Claim Gas & Oil Simulation Record

April 25, 2017

IBM, Nvidia, and Stone Ridge Technology today reported setting the performance record for a “billion cell” oil and gas reservoir simulation. Read more…

By John Russell

ASC17 Makes Splash at Wuxi Supercomputing Center

April 24, 2017

A record-breaking twenty student teams plus scores of company representatives, media professionals, staff and student volunteers transformed a formerly empty hall inside the Wuxi Supercomputing Center into a bustling hub of HPC activity, kicking off day one of 2017 Asia Student Supercomputer Challenge (ASC17). Read more…

By Tiffany Trader

HPE Extreme Performance Solutions

Remote Visualization Optimizing Life Sciences Operations and Care Delivery

As patients continually demand a better quality of care and increasingly complex workloads challenge healthcare organizations to innovate, investing in the right technologies is key to ensuring growth and success. Read more…

Groq This: New AI Chips to Give GPUs a Run for Deep Learning Money

April 24, 2017

CPUs and GPUs, move over. Thanks to recent revelations surrounding Google’s new Tensor Processing Unit (TPU), the computing world appears to be on the cusp of a new generation of chips designed specifically for deep learning workloads. Read more…

By Alex Woodie

Musk’s Latest Startup Eyes Brain-Computer Links

April 21, 2017

Elon Musk, the auto and space entrepreneur and severe critic of artificial intelligence, is forming a new venture that reportedly will seek to develop an interface between the human brain and computers. Read more…

By George Leopold

MIT Mathematician Spins Up 220,000-Core Google Compute Cluster

April 21, 2017

On Thursday, Google announced that MIT math professor and computational number theorist Andrew V. Sutherland had set a record for the largest Google Compute Engine (GCE) job. Sutherland ran the massive mathematics workload on 220,000 GCE cores using preemptible virtual machine instances. Read more…

By Tiffany Trader

NERSC Cori Shows the World How Many-Cores for the Masses Works

April 21, 2017

As its mission, the high performance computing center for the U.S. Department of Energy Office of Science, NERSC (the National Energy Research Supercomputer Center), supports a broad spectrum of forefront scientific research across diverse areas that includes climate, material science, chemistry, fusion energy, high-energy physics and many others. Read more…

By Rob Farber

Messina Update: The US Path to Exascale in 16 Slides

April 26, 2017

Paul Messina, director of the U.S. Exascale Computing Project, provided a wide-ranging review of ECP’s evolving plans last week at the HPC User Forum. Read more…

By John Russell

ASC17 Makes Splash at Wuxi Supercomputing Center

April 24, 2017

A record-breaking twenty student teams plus scores of company representatives, media professionals, staff and student volunteers transformed a formerly empty hall inside the Wuxi Supercomputing Center into a bustling hub of HPC activity, kicking off day one of 2017 Asia Student Supercomputer Challenge (ASC17). Read more…

By Tiffany Trader

Groq This: New AI Chips to Give GPUs a Run for Deep Learning Money

April 24, 2017

CPUs and GPUs, move over. Thanks to recent revelations surrounding Google’s new Tensor Processing Unit (TPU), the computing world appears to be on the cusp of a new generation of chips designed specifically for deep learning workloads. Read more…

By Alex Woodie

NERSC Cori Shows the World How Many-Cores for the Masses Works

April 21, 2017

As its mission, the high performance computing center for the U.S. Department of Energy Office of Science, NERSC (the National Energy Research Supercomputer Center), supports a broad spectrum of forefront scientific research across diverse areas that includes climate, material science, chemistry, fusion energy, high-energy physics and many others. Read more…

By Rob Farber

Hyperion (IDC) Paints a Bullish Picture of HPC Future

April 20, 2017

Hyperion Research – formerly IDC’s HPC group – yesterday painted a fascinating and complicated portrait of the HPC community’s health and prospects at the HPC User Forum held in Albuquerque, NM. HPC sales are up and growing ($22 billion, all HPC segments, 2016). Read more…

By John Russell

Knights Landing Processor with Omni-Path Makes Cloud Debut

April 18, 2017

HPC cloud specialist Rescale is partnering with Intel and HPC resource provider R Systems to offer first-ever cloud access to Xeon Phi "Knights Landing" processors. The infrastructure is based on the 68-core Intel Knights Landing processor with integrated Omni-Path fabric (the 7250F Xeon Phi). Read more…

By Tiffany Trader

CERN openlab Explores New CPU/FPGA Processing Solutions

April 14, 2017

Through a CERN openlab project known as the ‘High-Throughput Computing Collaboration,’ researchers are investigating the use of various Intel technologies in data filtering and data acquisition systems. Read more…

By Linda Barney

DOE Supercomputer Achieves Record 45-Qubit Quantum Simulation

April 13, 2017

In order to simulate larger and larger quantum systems and usher in an age of “quantum supremacy,” researchers are stretching the limits of today’s most advanced supercomputers. Read more…

By Tiffany Trader

Google Pulls Back the Covers on Its First Machine Learning Chip

April 6, 2017

This week Google released a report detailing the design and performance characteristics of the Tensor Processing Unit (TPU), its custom ASIC for the inference phase of neural networks (NN). Read more…

By Tiffany Trader

Quantum Bits: D-Wave and VW; Google Quantum Lab; IBM Expands Access

March 21, 2017

For a technology that’s usually characterized as far off and in a distant galaxy, quantum computing has been steadily picking up steam. Read more…

By John Russell

Trump Budget Targets NIH, DOE, and EPA; No Mention of NSF

March 16, 2017

President Trump’s proposed U.S. fiscal 2018 budget issued today sharply cuts science spending while bolstering military spending as he promised during the campaign. Read more…

By John Russell

HPC Compiler Company PathScale Seeks Life Raft

March 23, 2017

HPCwire has learned that HPC compiler company PathScale has fallen on difficult times and is asking the community for help or actively seeking a buyer for its assets. Read more…

By Tiffany Trader

Nvidia Responds to Google TPU Benchmarking

April 10, 2017

Nvidia highlights strengths of its newest GPU silicon in response to Google's report on the performance and energy advantages of its custom tensor processor. Read more…

By Tiffany Trader

CPU-based Visualization Positions for Exascale Supercomputing

March 16, 2017

In this contributed perspective piece, Intel’s Jim Jeffers makes the case that CPU-based visualization is now widely adopted and as such is no longer a contrarian view, but is rather an exascale requirement. Read more…

By Jim Jeffers, Principal Engineer and Engineering Leader, Intel

For IBM/OpenPOWER: Success in 2017 = (Volume) Sales

January 11, 2017

To a large degree IBM and the OpenPOWER Foundation have done what they said they would – assembling a substantial and growing ecosystem and bringing Power-based products to market, all in about three years. Read more…

By John Russell

TSUBAME3.0 Points to Future HPE Pascal-NVLink-OPA Server

February 17, 2017

Since our initial coverage of the TSUBAME3.0 supercomputer yesterday, more details have come to light on this innovative project. Of particular interest is a new board design for NVLink-equipped Pascal P100 GPUs that will create another entrant to the space currently occupied by Nvidia's DGX-1 system, IBM's "Minsky" platform and the Supermicro SuperServer (1028GQ-TXR). Read more…

By Tiffany Trader

Leading Solution Providers

Tokyo Tech’s TSUBAME3.0 Will Be First HPE-SGI Super

February 16, 2017

In a press event Friday afternoon local time in Japan, Tokyo Institute of Technology (Tokyo Tech) announced its plans for the TSUBAME3.0 supercomputer, which will be Japan’s “fastest AI supercomputer,” Read more…

By Tiffany Trader

Is Liquid Cooling Ready to Go Mainstream?

February 13, 2017

Lost in the frenzy of SC16 was a substantial rise in the number of vendors showing server oriented liquid cooling technologies. Three decades ago liquid cooling was pretty much the exclusive realm of the Cray-2 and IBM mainframe class products. That’s changing. We are now seeing an emergence of x86 class server products with exotic plumbing technology ranging from Direct-to-Chip to servers and storage completely immersed in a dielectric fluid. Read more…

By Steve Campbell

IBM Wants to be “Red Hat” of Deep Learning

January 26, 2017

IBM today announced the addition of TensorFlow and Chainer deep learning frameworks to its PowerAI suite of deep learning tools, which already includes popular offerings such as Caffe, Theano, and Torch. Read more…

By John Russell

Facebook Open Sources Caffe2; Nvidia, Intel Rush to Optimize

April 18, 2017

From its F8 developer conference in San Jose, Calif., today, Facebook announced Caffe2, a new open-source, cross-platform framework for deep learning. Caffe2 is the successor to Caffe, the deep learning framework developed by Berkeley AI Research and community contributors. Read more…

By Tiffany Trader

BioTeam’s Berman Charts 2017 HPC Trends in Life Sciences

January 4, 2017

Twenty years ago high performance computing was nearly absent from life sciences. Today it’s used throughout life sciences and biomedical research. Genomics and the data deluge from modern lab instruments are the main drivers, but so is the longer-term desire to perform predictive simulation in support of Precision Medicine (PM). There’s even a specialized life sciences supercomputer, ‘Anton’ from D.E. Shaw Research, and the Pittsburgh Supercomputing Center is standing up its second Anton 2 and actively soliciting project proposals. There’s a lot going on. Read more…

By John Russell

HPC Startup Advances Auto-Parallelization’s Promise

January 23, 2017

The shift from single core to multicore hardware has made finding parallelism in codes more important than ever, but that hasn’t made the task of parallel programming any easier. Read more…

By Tiffany Trader

HPC Technique Propels Deep Learning at Scale

February 21, 2017

Researchers from Baidu’s Silicon Valley AI Lab (SVAIL) have adapted a well-known HPC communication technique to boost the speed and scale of their neural network training and now they are sharing their implementation with the larger deep learning community. Read more…

By Tiffany Trader

IDG to Be Bought by Chinese Investors; IDC to Spin Out HPC Group

January 19, 2017

US-based publishing and investment firm International Data Group, Inc. (IDG) will be acquired by a pair of Chinese investors, China Oceanwide Holdings Group Co., Ltd. Read more…

By Tiffany Trader

  • arrow
  • Click Here for More Headlines
  • arrow
Share This