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ANATOMY: an analytical model of memory system performance

The 2014 ACM international conference on Measurement and modeling of computer systems
Austin, TX, USA, June 16--20, 2014


  1. Nagendra Dwarakanath Gulur, Supercomputer Education and Research Centre
  2. R. Govindarajan, Supercomputer Education and Research Centre; Department of Computer Science and Automation


Memory system design is increasingly influencing modern multi-core architectures from both performance and power perspectives. However predicting the performance of memory systems is complex, compounded by the myriad design choices and parameters along multiple dimensions, namely (i) technology, (ii) design and (iii) architectural choices. In this work, we construct an analytical model of the memory system to comprehend this diverse space and to study the impact of memory system parameters from latency and bandwidth perspectives. Our model, called ANATOMY, consists of two key components that are coupled with each other, to model the memory system accurately. The first component is a queuing model of memory which models in detail various design choices and captures the impact of technological choices in memory systems. The second component is an analytical model to summarize key workload characteristics, namely row buffer hit rate (RBH), bank-level parallelism (BLP), and request spread (S) which are used as inputs to the queuing model to estimate memory performance. We validate the model across a wide variety of memory configurations on 4, 8 and 16 cores using a total of 44 workloads. ANATOMY is able to predict memory latency with an average error of 8.1%, 4.1% and 9.7% over 4, 8 and 16 core configurations. We demonstrate the extensibility and applicability of our model by exploring a variety of memory design choices such as the impact of clock speed, benefit of multiple memory controllers, the role of banks and channel width, and so on. We also demonstrate ANATOMY's ability to capture architectural elements such as scheduling mechanisms (using FR_FCFS and PAR_BS) and impact of DRAM refresh cycles. In all of these studies, ANATOMY provides insight into sources of memory performance bottlenecks and is able to quantitatively predict the benefit of redressing them.


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