For emerging datacenter (in short, DC) workloads, such as online Internet services or offline
data analytics, how to evaluate the upper bound performance and provide apple-to-apple …

For emerging datacenter (in short, DC) workloads, such as online Internet services or offline
data analytics, how to evaluate the upper bound performance and provide apple-to-apple …

All computing infrastructure suffers from performance variability, be it bare-metal or
virtualized. This phenomenon originates from many sources: some transient, such as noisy …

Iterative optimization is a simple but powerful approach that searches the best possible
combination of compiler optimizations for a given workload. However, iterative optimization …

Traditional datacenter analysis is based on high-level, coarse-grained metrics. This
obscures our vision of datacenter behavior, as we do not observe the full picture nor …

AI benchmarking provides yardsticks for benchmarking, measuring and evaluating
innovative AI algorithms, architecture, and systems. Coordinated by BenchCouncil, this …

Performance evaluation is the basis for choosing appropriate system‐level configurations for
large‐scale data centers. While the change of a system‐level configuration would impact …

Recent trends in systems architecture include the growing importance of warehouse-sized
computers and new solutions to address the scalability and power efficiency challenges in …

As the amount of data explodes rapidly, more and more corporations are using data centers
to make effective decisions and gain a competitive edge. Data analysis applications play a …

Our global economy increasingly depends on our ability to gather, analyze, link, and
compare very large data sets. Keeping up with such big data poses challenges in terms of …