Statistical methodologies for the control of dynamic remapping

Following an initial mapping of a problem onto a multiprocessor machine or computer network, system performance often deteriorates with time. In order to maintain high performance, it may be necessary to remap the problem. The decision to remap must take into account measurements of performance deterioration, the cost of remapping, and the estimated benefits achieved by remapping. We examine the tradeoff between the costs and the benefits of remapping two qualitatively different kinds of problems. One problem assumes that performance deteriorates gradually, the other assumes that performance deteriorates suddenly. We consider a variety of policies for governing when to remap. In order to evaluate these policies, statistical models of problem behaviors are developed. Simulation results are presented which compare simple policies with computationally expensive optimal decision policies; these results demonstrate that for each problem type, the proposed simple policies are effective and robust

Adaptive Transactional Memories: Performance and Energy Consumption Tradeoffs

Energy efficiency is becoming a pressing issue, especially in large data centers where it entails, at the same time, a non-negligible management cost, an enhancement of hardware fault probability, and a significant environmental footprint. In this paper, we study how Software Transactional Memories (STM) can provide benefits on both power saving and the overall applications’ execution performance. This is related to the fact that encapsulating shared-data accesses within transactions gives the freedom to the STM middleware to both ensure consistency and reduce the actual data contention, the latter having been shown to affect the overall power needed to complete the application’s execution. We have selected a set of self-adaptive extensions to existing STM middlewares (namely, TinySTM and R-STM) to prove how self-adapting computation can capture the actual degree of parallelism and/or logical contention on shared data in a better way, enhancing even more the intrinsic benefits provided by STM. Of course, this benefit comes at a cost, which is the actual execution time required by the proposed approaches to precisely tune the execution parameters for reducing power consumption and enhancing execution performance. Nevertheless, the results hereby provided show that adaptivity is a strictly necessary requirement to reduce energy consumption in STM systems: Without it, it is not possible to reach any acceptable level of energy efficiency at all

Adaptive space-time sharing with SCOJO.

Coscheduling is a technique used to improve the performance of parallel computer applications under time sharing, i.e., to provide better response times than standard time sharing or space sharing. Dynamic coscheduling and gang scheduling are two main forms of coscheduling. In SCOJO (Share-based Job Coscheduling), we have introduced our own original framework to employ loosely coordinated dynamic coscheduling and a dynamic directory service in support of scheduling cross-site jobs in grid scheduling. SCOJO guarantees effective CPU shares by taking coscheduling effects into consideration and supports both time and CPU share reservation for cross-site job. However, coscheduling leads to high memory pressure and still involves problems like fragmentation and context-switch overhead, especially when applying higher multiprogramming levels. As main part of this thesis, we employ gang scheduling as more directly suitable approach for combined space-time sharing and extend SCOJO for clusters to incorporate adaptive space sharing into gang scheduling. We focus on taking advantage of moldable and malleable characteristics of realistic job mixes to dynamically adapt to varying system workloads and flexibly reduce fragmentation. In addition, our adaptive scheduling approach applies standard job-scheduling techniques like a priority and aging system, backfilling or easy backfilling. We demonstrate by the results of a discrete-event simulation that this dynamic adaptive space-time sharing approach can deliver better response times and bounded relative response times even with a lower multiprogramming level than traditional gang scheduling.Dept. of Computer Science. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .H825. Source: Masters Abstracts International, Volume: 43-01, page: 0237. Adviser: A. Sodan. Thesis (M.Sc.)--University of Windsor (Canada), 2004

CPU reservations and time constraints: implementation experience on windows NT

Journal ArticleThis paper presents an implementation of scheduling abstractions originally developed for the Rialto real-time operating system within a research version of Windows NT called Rialto/NT. These abstractions, CPU Reservations and Time Constraints, as described in the 1997 SOSP paper [Jones et al. 97], are intended to allow: (1) activities to obtain minimum guaranteed execution rates with application-specified reservation granularities via CPU Reservations, and (2) applications to schedule tasks by deadlines via Time Constraints, with on-time completion guaranteed for tasks with accepted constraints. The Rialto/NT scheduler differs from the original Rialto scheduler in several key respects. First, it has been extended to schedule multiprocessors-this is the primary new intellectual contribution of this work. It has been adapted to operate with operating system clock services that only provide timing interrupts at regular periodic intervals measured in milliseconds, rather than being able to schedule clock interrupts at arbitrary sub-millisecond points of time. It coexists with the existing Windows NT scheduler, allowing it to schedule time not scheduled by itself. Finally, it has been implemented in a particularly non-intrusive manner, using rather than replacing the existing Windows NT priority-based scheduler. Results presented will demonstrate that CPU Reservations and Time Constraints can be effectively implemented on multiprocessors. We will also describe the implementation techniques chosen and tradeoffs made as a result of implementing within Windows NT. Finally, we will present performance results and execution traces

Using hierarchical scheduling to support soft real-time applications in general-purpose operating systems

Journal ArticleThe CPU schedulers in general-purpose operating systems are designed to provide fast response time for interactive applications and high throughput for batch applications. The heuristics used to achieve these goals do not lend themselves to scheduling real-time applications, nor do they meet other scheduling requirements such as coordinating scheduling across several processors or machines, or enforcing isolation between applications, users, and administrative domains. Extending the scheduling subsystems of general-purpose operating systems in an ad hoc manner is time consuming and requires considerable expertise as well as source code to the operating system. Furthermore, once extended, the new scheduler may be as inflexible as the original. The thesis of this dissertation is that extending a general-purpose operating system with a general, heterogeneous scheduling hierarchy is feasible and useful. A hierarchy of schedulers generalizes the role of CPU schedulers by allowing them to schedule other schedulers in addition to scheduling threads. A general, heterogeneous scheduling hierarchy is one that allows arbitrary (or nearly arbitrary) scheduling algorithms throughout the hierarchy. In contrast, most of the previous work on hierarchical scheduling has imposed restrictions on the schedulers used in part or all of the hierarchy. This dissertation describes the Hierarchical Loadable Scheduler (HLS) architecture, which permits schedulers to be dynamically composed in the kernel of a general-purpose operating system. The most important characteristics of HLS, and the ones that distinguish it from previous work, are that it has demonstrated that a hierarchy of nearly arbitrary schedulers can be efficiently implemented in a general-purpose operating system, and that the behavior of a hierarchy of soft real-time schedulers can be reasoned about in order to provide guaranteed scheduling behavior to application threads. The flexibility afforded by HLS permits scheduling behavior to be tailored to meet complex requirements without encumbering users who have modest requirements with the performance and administrative costs of a complex scheduler. Contributions of this dissertation include the following. (1) The design, prototype implementation, and performance evaluation of HLS in Windows 2000. (2) A system of guarantees for scheduler composition that permits reasoning about the scheduling behavior of a hierarchy of soft real-time schedulers. Guarantees assure users that application requirements can be met throughout the lifetime of the application, and also provide application developers with a model of CPU allocation to which they can program. (3) The design, implementation, and evaluation of two augmented CPU reservation schedulers, which provide increase scheduling predictability when low-level operating system activity steals time from applications

Proceedings Work-In-Progress Session of the 13th Real-Time and Embedded Technology and Applications Symposium

The Work-In-Progress session of the 13th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS\u2707) presents papers describing contributions both to state of the art and state of the practice in the broad field of real-time and embedded systems. The 17 accepted papers were selected from 19 submissions. This proceedings is also available as Washington University in St. Louis Technical Report WUCSE-2007-17, at http://www.cse.seas.wustl.edu/Research/FileDownload.asp?733. Special thanks go to the General Chairs – Steve Goddard and Steve Liu and Program Chairs - Scott Brandt and Frank Mueller for their support and guidance

Preemptive Thread Block Scheduling with Online Structural Runtime Prediction for Concurrent GPGPU Kernels

Recent NVIDIA Graphics Processing Units (GPUs) can execute multiple kernels concurrently. On these GPUs, the thread block scheduler (TBS) uses the FIFO policy to schedule their thread blocks. We show that FIFO leaves performance to chance, resulting in significant loss of performance and fairness. To improve performance and fairness, we propose use of the preemptive Shortest Remaining Time First (SRTF) policy instead. Although SRTF requires an estimate of runtime of GPU kernels, we show that such an estimate of the runtime can be easily obtained using online profiling and exploiting a simple observation on GPU kernels' grid structure. Specifically, we propose a novel Structural Runtime Predictor. Using a simple Staircase model of GPU kernel execution, we show that the runtime of a kernel can be predicted by profiling only the first few thread blocks. We evaluate an online predictor based on this model on benchmarks from ERCBench, and find that it can estimate the actual runtime reasonably well after the execution of only a single thread block. Next, we design a thread block scheduler that is both concurrent kernel-aware and uses this predictor. We implement the SRTF policy and evaluate it on two-program workloads from ERCBench. SRTF improves STP by 1.18x and ANTT by 2.25x over FIFO. When compared to MPMax, a state-of-the-art resource allocation policy for concurrent kernels, SRTF improves STP by 1.16x and ANTT by 1.3x. To improve fairness, we also propose SRTF/Adaptive which controls resource usage of concurrently executing kernels to maximize fairness. SRTF/Adaptive improves STP by 1.12x, ANTT by 2.23x and Fairness by 2.95x compared to FIFO. Overall, our implementation of SRTF achieves system throughput to within 12.64% of Shortest Job First (SJF, an oracle optimal scheduling policy), bridging 49% of the gap between FIFO and SJF.Comment: 14 pages, full pre-review version of PACT 2014 poste