TCEC: Temperature and Energy-Constrained Scheduling in Real-Time Multitasking Systems

Qin, Xiaoke; Wang, Weixun; Mishra, Prabhat

doi:10.1109/tcad.2012.2190824

Cited by 12 publications

(3 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Wang el at. [90] proposed one such method for temperature and energy-constrained scheduling in multitasking systems. In order to deine constraints/thresholds, an important part of the design process is to estimate and analyse power consumption at diferent abstraction levels.…”

Section: Detecting Energy and Thermal Constraint Violationsmentioning

confidence: 99%

A Survey on Assertion-based Hardware Verification

et al. 2022

Self Cite

View full text Add to dashboard Cite

Hardware verification of modern electronic systems has been identified as a major bottleneck due to the increasing complexity and time-to-market constraints. One of the major objectives in hardware verification is to drastically reduce the validation and debug time without sacrificing the design quality. Assertion-based verification is a promising avenue for efficient hardware validation and debug. In this paper, we provide a comprehensive survey of recent progress in assertion-based hardware verification. Specifically, we outline how to define assertions using temporal logic to specify expected behaviors in different abstraction levels. Next, we describe state-of-the art approaches for automated generation of assertions. We also discuss test generation techniques for activating assertions to ensure that the generated assertions are valid. Finally, we present both pre-silicon and post-silicon assertion-based validation approaches that utilize simulation, formal methods as well as hybrid techniques. We conclude with a discussion on utilizing assertions for verifying both functional and non-functional requirements.

show abstract

Section: Detecting Energy and Thermal Constraint Violationsmentioning

confidence: 99%

A Survey on Assertion-based Hardware Verification

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…In a typical multicore system, each core maintains its private L1 caches while all cores share the same L2 cache. There are many optimization techniques for multi-level on-chip caches to improve 15:2 A. Ahmed et al performance and energy consumption of the overall system [11,15,24,27,28,30,42,46,48]. With the increasing demand for high reliability and availability, vulnerability of caches due to soft errors is gaining increasing importance.…”

Section: Introductionmentioning

confidence: 99%

Cache Reconfiguration Using Machine Learning for Vulnerability-aware Energy Optimization

Ahmed

Huang

Mishra

2019

ACM Trans. Embed. Comput. Syst.

Self Cite

View full text Add to dashboard Cite

Dynamic cache reconfiguration has been widely explored for energy optimization and performance improvement for single-core systems. Cache partitioning techniques are introduced for the shared cache in multicore systems to alleviate inter-core interference. While these techniques focus only on performance and energy, they ignore vulnerability due to soft errors. In this article, we present a static profiling based algorithm to enable vulnerability-aware energy-optimization for real-time multicore systems. Our approach can efficiently search the space of cache configurations and partitioning schemes for energy optimization while task deadlines and vulnerability constraints are satisfied. A machine learning technique has been employed to minimize the static profiling time without sacrificing the accuracy of results. Our experimental results demonstrate that our approach can achieve 19.2% average energy savings compared with the base configuration, while drastically reducing the vulnerability (49.3% on average) compared to state-of-the-art techniques. Furthermore, the machine learning technique enabled more than 10x speedup in static profiling time with a negligible prediction error of 3%.

show abstract

“…The low overhead in the OO-based scheduling is attractive in real-time cloud computing applications [15], [30]. In our previous work [40], the OO is also applied to the multiobjective scheduling (MOS) of many tasks in cloud platforms.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Workflow Scheduling on Cloud Computing Platforms with IterativeOrdinal Optimization

Zhang

Cao

Hwang

et al. 2015

IEEE Trans. Cloud Comput.

View full text Add to dashboard Cite

The scheduling of multitask jobs on clouds is an NP-hard problem. The problem becomes even worse when complex workflows are executed on elastic clouds, such as Amazon EC2 or IBM RC2. The main difficulty lies in the large search space and high overhead of generating optimal schedules, especially for real-time applications with dynamic workloads. In this work, a new iterative ordinal optimization (IOO) method is proposed. The ordinal optimization method is applied in each iteration to achieve suboptimal schedules. IOO aims at generating more efficient schedules from a global perspective over a long period. We prove through overhead analysis the advantages in time and space efficiency in using the IOO method. The IOO method is designed to adapt to system dynamism to yield suboptimal performance.In cloud experiments on IBM RC2 cloud, we execute 20,000 tasks in LIGO (Laser Interferometer Gravitational-wave Observatory) verification workflow on 128 virtual machines. The IOO schedule is generated in less than 1,000 seconds, while using the Monte Carlo simulation takes 27.6 hours, 100 times longer to yield an optimal schedule. The IOO-optimized schedule results in a throughput of 1,100 tasks/sec with 7 GB memory demand, compared with 60% decrease in throughput and 70% increase in memory demand in using the Monte Carlo method. Our LIGO experimental results clearly demonstrate the advantage of using the IOO-based workflow scheduling over the traditional blind-pick, ordinal optimization, or Monte Carlo methods. These numerical results are also validated by the theoretical complexity and overhead analysis provided.

show abstract

TCEC: Temperature and Energy-Constrained Scheduling in Real-Time Multitasking Systems

Cited by 12 publications

References 22 publications

A Survey on Assertion-based Hardware Verification

A Survey on Assertion-based Hardware Verification

Cache Reconfiguration Using Machine Learning for Vulnerability-aware Energy Optimization

Adaptive Workflow Scheduling on Cloud Computing Platforms with IterativeOrdinal Optimization

Contact Info

Product

Resources

About