No More Backstabbing... A Faithful Scheduling Policy for Multithreaded Programs

Pusukuri, Kishore Kumar; Gupta, Rajiv; Bhuyan, Laxmi N.

doi:10.1109/pact.2011.8

Cited by 10 publications

(9 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, lock contention (LOCK) for both BT and AP is high in all-cores configuration. Since BT and AP are CPU-intensive and high lock contention programs, and because of their high interaction in all-cores configuration, they experience high context-switch (CX) rate [Pusukuri et al 2011b;Johnson et al 2010]. In this experiment DTrace scripts were used for measuring RQ and CX-Rate [Cantrill et al 2004;.…”

Section: Cache Miss-ratio Vs Lock Contention Vs Thread Latencymentioning

confidence: 99%

“…FF policy reduces context-switch rate of APSI. Pusukuri et al 2011b]. We considered the use of both the Load Controller [Johnson et al 2010] and the FF policy [Pusukuri et al 2011b] as replacement of TS policy for dealing with lock contention during coscheduled runs.…”

Section: Scheduling Policy Vs Lockmentioning

confidence: 99%

“…Pusukuri et al 2011b]. We considered the use of both the Load Controller [Johnson et al 2010] and the FF policy [Pusukuri et al 2011b] as replacement of TS policy for dealing with lock contention during coscheduled runs. However, since Load Controller requires changes to application code, and its overhead increases linearly with the number of threads, we decided to make use of the FF policy.…”

Section: Scheduling Policy Vs Lockmentioning

confidence: 99%

See 2 more Smart Citations

Adapt

Pusukuri

Gupta

Bhuyan

2013

ACM Trans. Archit. Code Optim.

Self Cite

View full text Add to dashboard Cite

Since multicore systems offer greater performance via parallelism, future computing is progressing towards use of multicore machines with large number of cores. However, the performance of emerging multithreaded programs often does not scale to fully utilize the available cores. Therefore, simultaneously running multiple multithreaded applications becomes inevitable to fully exploit the computing potential of such machines. However, maximizing the performance and throughput on multicore machines in the presence of multiple multithreaded programs is a challenge for the OS. We have observed that the state-of-the-art contention management algorithms fail to effectively coschedule multithreaded programs on multicore machines. To address the above challenge, we present ADAPT, a scheduling framework that continuously monitors the resource usage of multithreaded programs and adaptively coschedules them such that they interfere with each other's performance as little as possible. In addition, ADAPT selects appropriate memory allocation and scheduling policies according to the workload characteristics. We have implemented ADAPT on a 64-core Supermicro server running Solaris 11 and evaluated it using 26 multithreaded programs including the TATP database application, SPECjbb2005, and programs from Phoenix, PARSEC, and SPEC OMP suites. The experimental results show that ADAPT substantially improves total turnaround time and system utilization relative to the default Solaris 11 scheduler.

show abstract

Section: Cache Miss-ratio Vs Lock Contention Vs Thread Latencymentioning

confidence: 99%

Section: Scheduling Policy Vs Lockmentioning

confidence: 99%

Section: Scheduling Policy Vs Lockmentioning

confidence: 99%

See 1 more Smart Citation

Adapt

Pusukuri

Gupta

Bhuyan

2013

ACM Trans. Archit. Code Optim.

Self Cite

View full text Add to dashboard Cite

show abstract

“…This solo characterization has linear complexity of O(n), which is much lower than O(n r ). Pusukuri et al [2011] described scheduling policy for minimizing lock contention for multithreaded applications. Emani et al [2013] determined the thread count to improve an application's performance in the presence of external workloads.…”

Section: Related Workmentioning

confidence: 99%

ReSense

Dey

Wang

Davidson

et al. 2013

ACM Trans. Archit. Code Optim.

View full text Add to dashboard Cite

To utilize the full potential of modern chip multiprocessors and obtain scalable performance improvements, it is critical to mitigate resource contention created by multithreaded workloads. In this article, we describe ReSense, the first runtime system that uses application characteristics to dynamically map multithreaded applications from dynamic workloads-workloads where multithreaded applications arrive, execute, and terminate continuously in unpredictable ways. ReSense mitigates contention for the shared resources in the memory hierarchy by applying a novel thread-mapping algorithm that dynamically adjusts the mapping of threads from dynamic workloads using a precalculated sensitivity score. The sensitivity score quantifies an application's sensitivity to sharing a particular memory resource and is calculated by an efficient characterization process that involves running the multithreaded application by itself on the target platform. To measure ReSense's effectiveness, sensitivity scores were determined for 21 benchmarks from PARSEC-2.1 and NPB-OMP-3.3 for the shared resources in the memory hierarchy on four different platforms. Using three different-sized dynamic workloads composed of randomly selected two, four, and eight corunning benchmarks with randomly selected start times, ReSense was able to improve the average response time of the three workloads by up to 27.03%, 20.89%, and 29.34% and throughput by up to 19.97%, 46.56%, and 29.86%, respectively, over the native OS on real hardware. By estimating and comparing ReSense's effectiveness with the optimal thread mapping for two different workloads, we found that the maximum average difference with the experimentally determined optimal performance was 1.49% for average response time and 2.08% for throughput.

show abstract

“…Pusukuri et al describes a scheduling policy for minimizing lock contention for multithreaded applications [62]. Emani et al determines the thread count to improve an application's performance in the presence of external workloads [63].…”

Section: Contention Mitigation For Multi-threaded Applicationsmentioning

confidence: 99%

ReSense: A Unified Framework for Improving Performance and Reliability in Multicore Architectures

Dey

View full text Add to dashboard Cite

Chip-multiprocessors (CMPs) have become ubiquitous in modern computing and the mainstream architecture for various platforms, including laptops, desktops, and large server machines. As technology scaling continues and more transistors are accommodated on the chip, the number of cores on CMPs is growing, and multi-core machines are scaling up to many-core machines. With this multi-core scaling, two major problems arise: shared-resource contention and soft errors or transient faults. Shared-resource contention can degrade an application's performance significantly, and soft errors increase the probability of incorrect application execution and the production of visible errors. To realize the full potential of multi-and many-core platforms, it is critical to ensure that applications in a workload not only execute e ciently and fast, but also correctly.In this dissertation, we develop a novel, general, and unified framework, ReSense, to address several challenges on multicore architectures including performance optimization, reliability improvement, power and thermal management. The framework includes five components: a general characterization methodology, a characterization metric, a sensitivity score, a thread mapping algorithm, and a run-time system. An instance of the framework is applied in two phases: characterization and mapping. The characterization phase utilizes the general characterization methodology and characterization metric to identify application characteristics without considering any co-runner(s). It generates a resource-sensitivity score for each application in a workload. In the mapping phase, the run-time system uses a thread-mapping algorithm and the sensitivity scores of the applications in a workload to c Abstract d determine the thread-mappings that optimize the objective function of the targeted problem.To demonstrate the utility and e ectiveness of ReSense, we use it to address the problems of shared-resource contention and soft errors for multi-threaded applications. For the resource contention problem, the characterization methodology determines how a multi-threaded application's performance is a ected as it shares a resource in the memory hierarchy. A sensitivity score based on resource contention is produced for each application in a workload.The run-time system uses the resource-contention sensitivity scores and a thread-mapping algorithm to allocate threads from a workload to core to mitigate shared-resource contention, thus improving response time and throughput.For the soft error problem, the characterization methodology determines how a multithreaded application's vulnerability to soft errors in shared caches is a ected by its resource occupancy duration. A sensitivity score based on cache occupancy is produced for each application in a workload. The run-time system uses the cache-occupancy sensitivity scores and a thread-mapping algorithm to allocate workload threads to cores to reduce the occupancy in the shared caches, thus reducing cache vulnerability.Both minimizing...

show abstract

No More Backstabbing... A Faithful Scheduling Policy for Multithreaded Programs

Cited by 10 publications

References 21 publications

Adapt

Adapt

ReSense

ReSense: A Unified Framework for Improving Performance and Reliability in Multicore Architectures

Contact Info

Product

Resources

About