ARM processors have dominated the mobile device market in the last decade due to their favorable computing to energy ratio. In this age of Cloud data centers and Big Data analytics, the focus is increasingly on power efficient processing, rather than just high throughput computing. ARM's first commodity server-grade processor is the recent AMD A1100-series processor, based on a 64-bit ARM Cortex A57 architecture. In this paper, we study the performance and energy efficiency of a server based on this ARM64 CPU, relative to a comparable server running an AMD Opteron 3300-series x64 CPU, for Big Data workloads. Specifically, we study these for Intel's HiBench suite of web, query and machine learning benchmarks on Apache Hadoop v2.7 in a pseudo-distributed setup, for data sizes up to @math files, @math web pages and @math tuples. Our results show that the ARM64 server's runtime performance is comparable to the x64 server for integer-based workloads like Sort and Hive queries, and only lags behind for floating-point intensive benchmarks like PageRank, when they do not exploit data parallelism adequately. We also see that the ARM64 server takes 1 3rd the energy, and has an Energy Delay Product (EDP) that is @math lower than the x64 server. These results hold significant promise for data centers hosting ARM64 servers to reduce their operational costs, while offering a competitive performance for Big Data workloads.
Query processing (TPC-C and TPC-H on MySQL) and Big Data (K-Means, TeraSort and WordCount) benchmarks have been tried on the ARM Cortex-A7 little ARM Cortex-A15 big cores, to compare against Intel Xeon servers @cite_16 . They evaluate execution time, energy usage and total cost of running the workloads on these self-hosted ARM and Xeon nodes. Their results show that ARM takes more time to perform MapReduce computations compared to others, when implemented in Java. However, this time is reduced significantly by the C++ implementation of the same. Our study shows similar limitations with floating-point performance in ARM64 as well.
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The continuous increase in volume, variety and velocity of Big Data exposes datacenter resource scaling to an energy utilization problem. Traditionally, datacenters employ x86-64 (big) server nodes with power usage of tens to hundreds of Watts. But lately, low-power (small) systems originally developed for mobile devices have seen significant improvements in performance. These improvements could lead to the adoption of such small systems in servers, as announced by major industry players. In this context, we systematically conduct a performance study of Big Data execution on small nodes in comparison with traditional big nodes, and present insights that would be useful for future development. We run Hadoop MapReduce, MySQL and in-memory Shark workloads on clusters of ARM big. LITTLE boards and Intel Xeon server systems. We evaluate execution time, energy usage and total cost of running the workloads on self-hosted ARM and Xeon nodes. Our study shows that there is no one size fits all rule for judging the efficiency of executing Big Data workloads on small and big nodes. But small memory size, low memory and I O bandwidths, and software immaturity concur in canceling the lower-power advantage of ARM servers. We show that I O-intensive MapReduce workloads are more energy-efficient to run on Xeon nodes. In contrast, database query processing is always more energy-efficient on ARM servers, at the cost of slightly lower throughput. With minor software modifications, CPU-intensive MapReduce workloads are almost four times cheaper to execute on ARM servers.