910 research outputs found
Energy challenges for ICT
The energy consumption from the expanding use of information and communications technology (ICT) is unsustainable with present drivers, and it will impact heavily on the future climate change. However, ICT devices have the potential to contribute signi - cantly to the reduction of CO2 emission and enhance resource e ciency in other sectors, e.g., transportation (through intelligent transportation and advanced driver assistance systems and self-driving vehicles), heating (through smart building control), and manu- facturing (through digital automation based on smart autonomous sensors). To address the energy sustainability of ICT and capture the full potential of ICT in resource e - ciency, a multidisciplinary ICT-energy community needs to be brought together cover- ing devices, microarchitectures, ultra large-scale integration (ULSI), high-performance computing (HPC), energy harvesting, energy storage, system design, embedded sys- tems, e cient electronics, static analysis, and computation. In this chapter, we introduce challenges and opportunities in this emerging eld and a common framework to strive towards energy-sustainable ICT
ArrayBridge: Interweaving declarative array processing with high-performance computing
Scientists are increasingly turning to datacenter-scale computers to produce
and analyze massive arrays. Despite decades of database research that extols
the virtues of declarative query processing, scientists still write, debug and
parallelize imperative HPC kernels even for the most mundane queries. This
impedance mismatch has been partly attributed to the cumbersome data loading
process; in response, the database community has proposed in situ mechanisms to
access data in scientific file formats. Scientists, however, desire more than a
passive access method that reads arrays from files.
This paper describes ArrayBridge, a bi-directional array view mechanism for
scientific file formats, that aims to make declarative array manipulations
interoperable with imperative file-centric analyses. Our prototype
implementation of ArrayBridge uses HDF5 as the underlying array storage library
and seamlessly integrates into the SciDB open-source array database system. In
addition to fast querying over external array objects, ArrayBridge produces
arrays in the HDF5 file format just as easily as it can read from it.
ArrayBridge also supports time travel queries from imperative kernels through
the unmodified HDF5 API, and automatically deduplicates between array versions
for space efficiency. Our extensive performance evaluation in NERSC, a
large-scale scientific computing facility, shows that ArrayBridge exhibits
statistically indistinguishable performance and I/O scalability to the native
SciDB storage engine.Comment: 12 pages, 13 figure
Characterizing Power and Energy Efficiency of Legion Data-Centric Runtime and Applications on Heterogeneous High-Performance Computing Systems
The traditional parallel programming models require programmers to explicitly specify parallelism and data movement of the underlying parallel mechanisms. Different from the traditional computation-centric programming, Legion provides a data-centric programming model for extracting parallelism and data movement. In this chapter, we aim to characterize the power and energy consumption of running HPC applications on Legion. We run benchmark applications on compute nodes equipped with both CPU and GPU, and measure the execution time, power consumption and CPU/GPU utilization. Additionally, we test the message passing interface (MPI) version of these applications and compare the performance and power consumption of high-performance computing (HPC) applications using the computation-centric and data-centric programming models. Experimental results indicate Legion applications outperforms MPI applications on both performance and energy efficiency, i.e., Legion applications can be 9.17 times as fast as MPI applications and use only 9.2% energy. Legion effectively explores the heterogeneous architecture and runs applications tasks on GPU. As far as we know, this is the first study to understand the power and energy consumption of Legion programming and runtime infrastructure. Our findings will enable HPC system designers and operators to develop and tune the performance of data-centric HPC applications with constraints on power and energy consumption
Sustainable HPC: Modeling, Characterization, and Implications of Carbon Footprint in Modern HPC Systems
The rapid growth in demand for HPC systems has led to a rise in energy
consumption and carbon emissions, which requires urgent intervention. In this
work, we present a comprehensive framework for analyzing the carbon footprint
of high-performance computing (HPC) systems, considering the carbon footprint
during both the hardware production and system operational stages. Our work
employs HPC hardware component carbon footprint modeling, regional carbon
intensity analysis, and experimental characterization of the system life cycle
to highlight the importance of quantifying the carbon footprint of an HPC
system holistically
ASCR/HEP Exascale Requirements Review Report
This draft report summarizes and details the findings, results, and
recommendations derived from the ASCR/HEP Exascale Requirements Review meeting
held in June, 2015. The main conclusions are as follows. 1) Larger, more
capable computing and data facilities are needed to support HEP science goals
in all three frontiers: Energy, Intensity, and Cosmic. The expected scale of
the demand at the 2025 timescale is at least two orders of magnitude -- and in
some cases greater -- than that available currently. 2) The growth rate of data
produced by simulations is overwhelming the current ability, of both facilities
and researchers, to store and analyze it. Additional resources and new
techniques for data analysis are urgently needed. 3) Data rates and volumes
from HEP experimental facilities are also straining the ability to store and
analyze large and complex data volumes. Appropriately configured
leadership-class facilities can play a transformational role in enabling
scientific discovery from these datasets. 4) A close integration of HPC
simulation and data analysis will aid greatly in interpreting results from HEP
experiments. Such an integration will minimize data movement and facilitate
interdependent workflows. 5) Long-range planning between HEP and ASCR will be
required to meet HEP's research needs. To best use ASCR HPC resources the
experimental HEP program needs a) an established long-term plan for access to
ASCR computational and data resources, b) an ability to map workflows onto HPC
resources, c) the ability for ASCR facilities to accommodate workflows run by
collaborations that can have thousands of individual members, d) to transition
codes to the next-generation HPC platforms that will be available at ASCR
facilities, e) to build up and train a workforce capable of developing and
using simulations and analysis to support HEP scientific research on
next-generation systems.Comment: 77 pages, 13 Figures; draft report, subject to further revisio
RUNTIME METHODS TO IMPROVE ENERGY EFFICIENCY IN SUPERCOMPUTING APPLICATIONS
Energy efficiency in supercomputing is critical to limit operating costs and carbon footprints. While the energy efficiency of future supercomputing centers needs to improve at all levels, the energy consumed by the processing units is a large fraction of the total energy consumed by High Performance Computing (HPC) systems. HPC applications use a parallel programming paradigm like the Message Passing Interface (MPI) to coordinate computation and communication among thousands of processors. With dynamically-changing factors both in hardware and software affecting energy usage of processors, there exists a need for power monitoring and regulation at runtime to achieve savings in energy.
This dissertation highlights an adaptive runtime framework that enables processors with core-specific power control by dynamically adapting to workload characteristics to reduce power with little or no performance impact. Two opportunities to improve the energy efficiency of processors running MPI applications are identified - computational workload imbalance and waiting on memory. Monitoring of performance and power regulation is performed by the framework transparently within the MPI runtime system, eliminating the need for code changes to MPI applications. The effect of enforcing power limits (capping) on processors is also investigated.
Experiments on 32 nodes (1024 cores) show that in presence of workload imbalance, the runtime reduces Central Processing Unit (CPU) frequency on cores not on the critical path, thereby reducing power and hence energy usage without deteriorating performance. Using this runtime, six MPI mini-applications and a full MPI application show an overall 20% decrease in energy use with less than 1% increase in execution time. In addition, the lowering of frequency on non-critical cores reduces run-to-run performance variation and improves performance. For the full application, an average speedup of 11% is seen, while the power is lowered by about 31% for an energy savings of up to 42%. Another experiment on 16 nodes (256 cores) that are power capped also shows performance improvement along with power reduction. Thus, energy optimization can also be a performance optimization. For applications that are limited by memory access times, memory metrics identified facilitate lowering of power by up to 32% without adversely impacting performance.Doctor of Philosoph
Cloud computing: survey on energy efficiency
International audienceCloud computing is today’s most emphasized Information and Communications Technology (ICT) paradigm that is directly or indirectly used by almost every online user. However, such great significance comes with the support of a great infrastructure that includes large data centers comprising thousands of server units and other supporting equipment. Their share in power consumption generates between 1.1% and 1.5% of the total electricity use worldwide and is projected to rise even more. Such alarming numbers demand rethinking the energy efficiency of such infrastructures. However, before making any changes to infrastructure, an analysis of the current status is required. In this article, we perform a comprehensive analysis of an infrastructure supporting the cloud computing paradigm with regards to energy efficiency. First, we define a systematic approach for analyzing the energy efficiency of most important data center domains, including server and network equipment, as well as cloud management systems and appliances consisting of a software utilized by end users. Second, we utilize this approach for analyzing available scientific and industrial literature on state-of-the-art practices in data centers and their equipment. Finally, we extract existing challenges and highlight future research directions
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