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Energy efficiency is of steadily growing importance in virtually all areas from mobile to high performance computing. Therefore, lots of research projects focus on this topic and strongly rely on power measurements from their test platforms. The need for finer grained measurement data-both in terms of temporal and spatial resolution (component breakdown)-often collides with very rudimentary measurement setups that rely e.g., on non-professional power meters, IMPI based platform data or model-based interfaces such as RAPL or APM. This paper presents an in-depth study of several different AC and DC measurement methodologies as well as model approaches on test systems with the latest processor generations from both Intel and AMD. We analyze most important aspects such as signal quality, time resolution, accuracy, and measurement overhead and use a calibrated, professional power analyzer as our reference.

Across a broad range of applications, multicore technology is the most important factor that drives today's microprocessor performance improvements. Closely coupled is a growing complexity of the memory subsystems with several cache levels that need to be exploited efficiently to gain optimal application performance. Many important implementation details of these memory subsystems are undocumented. We therefore present a set of sophisticated benchmarks for latency and bandwidth measurements to arbitrary locations in the memory subsystem. We consider the coherency state of cache lines to analyze the cache coherency protocols and their performance impact. The potential of our approach is demonstrated with an in-depth comparison of ccNUMA multiprocessor systems with AMD (Shanghai) and Intel (Nehalem-EP) quad-core x86-64 processors that both feature integrated memory controllers and coherent point-to-point interconnects. Using our benchmarks we present fundamental memory performance data and architectural properties of both processors. Our comparison reveals in detail how the microarchitectural differences tremendously affect the performance of the memory subsystem.

Application performance on multicore processors is seldom constrained by the speed of floating point or integer units. Much more often, limitations are caused by the memory subsystem, particularly shared resources such as last level caches or memory controllers. Measuring, predicting and modeling memory performance becomes a steeper challenge with each new processor generation due to the growing complexity and core count. We tackle the important aspect of measuring and understanding undocumented memory performance numbers in order to create valuable insight into microprocessor details. For this, we build upon a set of sophisticated benchmarks that support latency and bandwidth measurements to arbitrary locations in the memory subsystem. These benchmarks are extended to support AVX instructions for bandwidth measurements and to integrate the coherence states (O)wned and (F)orward. We then use these benchmarks to perform an indepth analysis of current ccNUMA multiprocessor systems with Intel (Sandy Bridge-EP) and AMD (Bulldozer) processors. Using our benchmarks we present fundamental memory performance data and illustrate performance-relevant architectural properties of both designs.