Allan Snavely

Bio: Allan Snavely is an academic researcher from University of California, San Diego. The author has contributed to research in topics: Supercomputer & Benchmark (computing). The author has an hindex of 34, co-authored 87 publications receiving 4032 citations. Previous affiliations of Allan Snavely include Lawrence Livermore National Laboratory & San Diego Supercomputer Center.

Symbiotic jobscheduling for a simultaneous multithreaded processor

Abstract: Simultaneous Multithreading machines fetch and execute instructions from multiple instruction streams to increase system utilization and speedup the execution of jobs. When there are more jobs in the system than there is hardware to support simultaneous execution, the operating system scheduler must choose the set of jobs to coscheduleThis paper demonstrates that performance on a hardware multithreaded processor is sensitive to the set of jobs that are coscheduled by the operating system jobscheduler. Thus, the full benefits of SMT hardware can only be achieved if the scheduler is aware of thread interactions. Here, a mechanism is presented that allows the scheduler to significantly raise the performance of SMT architectures. This is done without any advance knowledge of a workload's characteristics, using sampling to identify jobs which run well together.We demonstrate an SMT jobscheduler called SOS. SOS combines an overhead-free sample phase which collects information about various possible schedules, and a symbiosis phase which uses that information to predict which schedule will provide the best performance. We show that a small sample of the possible schedules is sufficient to identify a good schedule quickly. On a system with random job arrivals and departures, response time is improved as much as 17% over a schedule which does not incorporate symbiosis.

A Framework for Performance Modeling and Prediction

Abstract: Cycle-accurate simulation is far too slow for modeling the expected performance of full parallel applications on large HPC systems. And just running an application on a system and observing wallclock time tells you nothing about why the application performs as it does (and is anyway impossible on yet-to-be-built systems). Here we present a framework for performance modeling and prediction that is faster than cycle-accurate simulation, more informative than simple benchmarking, and is shown useful for performance investigations in several dimensions.

Software Challenges in Extreme Scale Systems

Abstract: Computer systems anticipated in the 2015 - 2020 timeframe are referred to as Extreme Scale because they will be built using massive multi-core processors with 100's of cores per chip. The largest capability Extreme Scale system is expected to deliver Exascale performance of the order of 10 18 operations per second. These systems pose new critical challenges for software in the areas of concurrency, energy eciency and resiliency. In this paper, we discuss the implications of the concurrency and energy eciency challenges on future software for Extreme Scale Systems. From an application viewpoint, the concurrency and energy challenges boil down to the ability to express and manage parallelism and locality by exploring a range of strong scaling and new-era weak scaling techniques. For expressing parallelism and locality, the key challenges are the ability to expose all of the intrinsic parallelism and locality in a programming model, while ensuring that this expression of parallelism and locality is portable across a range of systems. For managing parallelism and locality, the OS-related challenges include parallel scalability, spatial partitioning of OS and application functionality, direct hardware access for inter-processor communication, and asynchronous rather than interrupt-driven events, which are accompanied by runtime system challenges for scheduling, synchronization, memory management, communication, performance monitoring, and power management. We conclude by discussing the importance of software-hardware co- design in addressing the fundamental challenges for application enablement on Extreme Scale systems.

Symbiotic jobscheduling with priorities for a simultaneous multithreading processor

Abstract: Simultaneous Multithreading machines benefit from jobscheduling software that monitors how well coscheduled jobs share CPU resources, and coschedules jobs that interact well to make more efficient use of those resources. As a result, informed coscheduling can yield significant performance gains over naive schedulers. However, prior work on coscheduling focused on equal-priority job mixes, which is an unrealistic assumption for modern operating systems.This paper demonstrates that a scheduler for an SMT machine can both satisfy process priorities and symbiotically schedule low and high priority threads to increase system throughput. Naive priority schedulers dedicate the machine to high priority jobs to meet priority goals, and as a result decrease opportunities for increased performance from multithreading and coscheduling. More informed schedulers, however, can dynamically monitor the progress and resource utilization of jobs on the machine, and dynamically adjust the degree of multithreading to improve performance while still meeting priority goals.Using detailed simulation of an SMT architecture, we introduce and evaluate a series of five software and hardware-assisted priority schedulers. Overall, our results indicate that coscheduling priority jobs can significantly increase system throughput by as much as 40%, and that (1) the benefit depends upon the relative priority of the coscheduled jobs, and (2) more sophisticated schedulers are more effective when the differences in priorities are greatest. We show that our priority schedulers can decrease average turnaround times for a random jobmix by as much as 33%.

Are user runtime estimates inherently inaccurate

Abstract: Computer system batch schedulers typically require information from the user upon job submission, including a runtime estimate. Inaccuracy of these runtime estimates, relative to the actual runtime of the job, has been well documented and is a perennial problem mentioned in the job scheduling literature. Typically users provide these estimates under circumstances where their job will be killed after the provided amount of time elapses. Also, users may be unaware of the potential benefits of providing accurate estimates, such as increased likelihood of backfilling. This study examines user behavior when the threat of job killing is removed, and when a tangible reward for accuracy is provided. We show that under these conditions, about half of users provide an improved estimate, but there is not a substantial improvement in the overall average accuracy.

Fast parallel algorithms for short-range molecular dynamics

Abstract: Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

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Roofline: an insightful visual performance model for multicore architectures

Abstract: The Roofline model offers insight on how to improve the performance of software and hardware.

The future of microprocessors

Abstract: Energy efficiency is the new fundamental limiter of processor performance, way beyond numbers of processors.