Abstract
Power consumption will be a key constraint on the future growth of Distributed High Throughput Computing (DHTC) as used by High Energy Physics (HEP). This makes performance-per-watt a crucial metric for selecting cost-efficient computing solutions. For this paper, we have done a wide survey of current and emerging architectures becoming available on the market including x86-64 variants, ARMv7 32-bit, ARMv8 64-bit, Many-Core and GPU solutions, as well as newer System-on-Chip (SoC) solutions. We compare performance and energy efficiency using an evolving set of standardized HEP-related benchmarks and power measurement techniques we have been developing. We evaluate the potential for use of such computing solutions in the context of DHTC systems, such as the Worldwide LHC Computing Grid (WLCG).
Highlights
The data produced by the four experiments at the Large Hadron Collider (LHC) [1] or similar High Energy Physics (HEP) experiments requires a significant amount of human and computing resources which cannot be provided by research institute or even country
For this reasons the various parties involved created the Worldwide LHC Computing Grid (WLCG) in order to tackle the data processing challenges posed by such a large amount of data
Test Environments for Power and Performance Measurements We describe the test environments we have used to do power and performance measurements for two Intel Xeon processors, belonging respectively to the Sandy Bridge generation and to the Haswell one, an Applied Micro (APM) ARMv8 64-bit X-Gene1 Server-on-Chip, an Intel Xeon Phi coprocessor, an Intel Atom processor of Avoton class, an IBM POWER8 processor
Summary
The data produced by the four experiments at the Large Hadron Collider (LHC) [1] or similar High Energy Physics (HEP) experiments requires a significant amount of human and computing resources which cannot be provided by research institute or even country. For this reasons the various parties involved created the Worldwide LHC Computing Grid (WLCG) in order to tackle the data processing challenges posed by such a large amount of data.
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