The ASPLOS tutorials on Sunday, March 17th are cancelled due to low registration. There was a large registration for the co-located conference and workshops during March 16-17 instead.
- Tutorial I: The Open Community Runtime and its use in Systems Research
- Tutorial II: Using Queuing Theory to Model Data Center Systems
Sunday, 17th March 2013
8:30am – 12:00pm, Tutorial I: The Open Community Runtime and its use in Systems Research
- Rob Knauerhase, Intel Corporation, rob.knauerhase@intel.com
- Vivek Sarkar, Rice University, vsarkar@rice.edu
Future many-core platforms will impose a fresh set of challenges on runtime systems that include targeting nodes with hundreds of homogeneous and heterogeneous cores, as well as energy, data movement and resiliency constraints within and across nodes. The Open Community Runtime (OCR) provides a runtime system framework within which to explore how fine-grained event-driven tasks, movable data blocks and dynamic resource adaptions can address these challenges.
OCR is an open-source project that includes components for task scheduling and resource mapping in homogeneous, heterogeneous, and distributed environments. In addition to native support on current platforms, OCR includes the ability to emulate different processor and memory features (e.g. scratchpad memories, cache policies, and deep hierarchical arrangements of memory), as well as processor/memory interconnects and communication pathways. The runtime includes facilities for introspection of system behavior, and a language with which a programmer (or a tuning expert familiar with the machine) can express hints about at-runtime optimizations.
The tutorial will introduce OCR’s concepts and provide a demonstration of the latest open source release (https://01.org/projects/open-community-runtime). We will show how to use OCR for existing systems and upcoming processor simulators, and point to future areas in which members of the community can contribute (or are already contributing) components for academic and industrial research. Our goal for OCR is to help enable community-wide innovation in programming systems above the OCR level, in hardware designs below the OCR level, and in runtime systems at the OCR level.
Partial support for the OCR project was provided through the XStack program of the U.S. Department of Energy, Office of Science, Advanced Scientific Computing Research (ASCR). Additional sources of support include the UHPC program of the U.S. Department of Defense’s Advanced Research Projects Agency (DARPA), and Intel Corporation/Intel Labs.
1:30pm – 5:00pm, Tutorial II: Using Queuing Theory to Model Data Center Systems
- David Meisner, Facebook, meisner@fb.com
- Mor Harchol-Balter, Carnegie Mellon University, harchol@cs.cmu.edu
- Thomas Wenisch, University of Michigan, twenisch@umich.edu
Recently, there has been an explosive growth in Internet services, greatly increasing the importance of data center systems. Applications served from “the cloud” are driving data center growth and quickly overtaking traditional workstations. Although there are many analytic and simulation tools for evaluating components of desktop and server architectures in detail, scalable modeling tools are noticeably missing.
We believe that stochastic methods and queueing theory together provide an avenue to answer important questions about data center systems. In the first half of this tutorial, we present a crash-course (or perhaps, a refresher for some) on the essential elements of queueing theory with particular applications to modeling data center systems. We also illustrate how queueing theory can be used to solve problems related to the design and analysis of computer systems.
In the second part of the tutorial, we describe BigHouse, a simulation infrastructure that combines queuing theory and stochastic methods to model data centers systems. Instead of simulating servers using detailed microarchitectural models, BigHouse raises the level of abstraction using the tools of queuing theory, enabling simulation at 1000-server scale in less than an hour. We include brief background on data center power modeling, a description of the statistical methods used by BigHouse, parallelization techniques, a tour of the simulator code, and a case study of using BigHouse to model data center power capping.
BigHouse is described in the following publication: D. Meisner, J. Wu, T. F. Wenisch.
BigHouse: A simulation infrastructure for data center systems. Proceedings of the International Symposium on Performance Analysis of Systems and Software (ISPASS). Best Paper Award, Apr. 2012.