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We benchmarked an ARM cortex-A9 based server system with a four-core CPU running at 1.1 GHz. The system used Ubuntu 12.04 as operating system and the HEPSPEC 2006 (HS06) benchmarking suite was compiled natively with gcc-4.4 on the system. The benchmark was run for various settings of the relevant gcc compiler options. We did not find significant influence from the compiler options on the benchmark result. The final HS06 benchmark result is 10.4.
To keep up with demand, servers will scale up to handle hundreds of thousands of clients simultaneously. Much of the focus of the community has been on scaling servers in terms of aggregate traffic intensity (packets transmitted per second). However,
Machine learning (ML) needs industry-standard performance benchmarks to support design and competitive evaluation of the many emerging software and hardware solutions for ML. But ML training presents three unique benchmarking challenges absent from o
Machine-learning (ML) hardware and software system demand is burgeoning. Driven by ML applications, the number of different ML inference systems has exploded. Over 100 organizations are building ML inference chips, and the systems that incorporate ex
Edge computing has been developed to utilize multiple tiers of resources for privacy, cost and Quality of Service (QoS) reasons. Edge workloads have the characteristics of data-driven and latency-sensitive. Because of this, edge systems have develope
The Linked Data Benchmark Councils Social Network Benchmark (LDBC SNB) is an effort intended to test various functionalities of systems used for graph-like data management. For this, LDBC SNB uses the recognizable scenario of operating a social netwo