Degree of parallelism

About: Degree of parallelism is a research topic. Over the lifetime, 1515 publications have been published within this topic receiving 25546 citations.

Hybrid functional and instruction level power modeling for embedded processors

Abstract: In this contribution the concept of Functional-Level Power Analysis (FLPA) for power estimation of programmable processors is extended in order to model even embedded general purpose processors. The basic FLPA approach is based on the separation of the processor architecture into functional blocks like e.g. processing unit, clock network, internal memory etc. The power consumption of these blocks is described by parameterized arithmetic models. By application of a parser based automated analysis of assembler codes the input parameters of the arithmetic functions like e.g. the achieved degree of parallelism or the kind and number of memory accesses can be computed. For modeling an embedded general purpose processor (here, an ARM940T) the basic FLPA modeling concept had to be extended to a so-called hybrid functional level and instruction level model in order to achieve a good modeling accuracy. The approach is exemplarily demonstrated and evaluated applying a variety of basic digital signal processing tasks ranging from basic filters to complete audio decoders. Estimated power figures for the inspected tasks are compared to physically measured values. A resulting maximum estimation error of less than 8 % is achieved.

Fuzzy fairness controller for NVMe SSDs

Abstract: Modern NVMe SSDs are widely deployed in diverse domains due to characteristics like high performance, robustness, and energy efficiency. It has been observed that the impact of interference among the concurrently running workloads on their overall response time differs significantly in these devices, which leads to unfairness. Workload intensity is a dominant factor influencing the interference. Prior works use a threshold value to characterize a workload as high-intensity or low-intensity; this type of characterization has drawbacks due to lack of information about the degree of low- or high-intensity. A data cache in an SSD controller - usually based on DRAMs - plays a crucial role in improving device throughput and lifetime. However, the degree of parallelism is limited at this level compared to the SSD back-end consisting of several channels, chips, and planes. Therefore, the impact of interference can be more pronounced at the data cache level. No prior work has addressed the fairness issue at the data cache level to the best of our knowledge. In this work, we address this issue by proposing a fuzzy logic-based fairness control mechanism. A fuzzy fairness controller characterizes the degree of flow intensity (i.e., the rate at which requests are generated) of a workload and assigns priorities to the workloads. We implement the proposed mechanism in the MQSim framework and observe that our technique improves the fairness, weighted speedup, and harmonic speedup of SSD by 29.84%, 11.24%, and 24.90% on average over state of the art, respectively. The peak gains in fairness, weighted speedup, and harmonic speedup are 2.02x, 29.44%, and 56.30%, respectively.

A system-centric metric for the evaluation of online job schedules

Abstract: The paper discusses a rarely used metric that is well suited to evaluate online schedules for independent jobs on massively parallel processors. The metric is based on the total weighted completion time objective with the weight being the resource consumption of the job. Although every job contributes to the objective value, the metric exhibits many properties that are similar to the properties of the makespan objective. For this metric, we particularly address nonclairvoyant online scheduling of sequential jobs on parallel identical machines and prove an almost tight competitive factor of 1.25 for nondelay schedules. For the extension of the problem to rigid parallel jobs, we show that no constant competitive factor exists. However, if all jobs are released at time 0, List Scheduling in descending order of the degree of parallelism guarantees an approximation factor of 2.

Optimal Degree of Parallelism and Integration in Design and Process Planning

Abstract: Efficiency in design and process planning is defined as measured contribution to goal fulfilment. In order to identify integration measures for design and process planning with highest effect on efficiency, an evaluation methodology has been developed. Examples of integration measures are early transmission and feedback of information, coordination and use of integrating methods as QFD or FMEA. The developed evaluation methodology supports the decision between alternative parallel and integrated procedures by estimating their efforts, benefits and risks. Applying the proposed evaluation methodology ensures an optimised degree of integration and parallelism in design and process planning. Thus integration measures can be used with full effect on reducing time-to-market, trimming development and manufacturing cost and enhancing product quality.

Evaluation of Distributed Machine Learning Algorithms for Anomaly Detection from Large-Scale System Logs: A Case Study

Abstract: Anomaly detection is a valuable feature for detecting and diagnosing faults in large-scale, distributed systems. These systems usually provide tens of millions of lines of logs that can be exploited for this purpose. However, centralized implementations of traditional machine learning algorithms fall short to analyze this data in a scalable manner. One way to address this challenge is to employ distributed systems to analyze the immense amount of logs generated by other distributed systems. We conducted a case study to evaluate two unsupervised machine learning algorithms for this purpose on a benchmark dataset. In particular, we evaluated distributed implementations of PCA and K-means algorithms. We compared the accuracy and performance of these algorithms both with respect to each other and with respect to their centralized implementations. Results showed that the distributed versions can achieve the same accuracy and provide a performance improvement by orders of magnitude when compared to their centralized versions. The performance of PCA turns out to be better than K-means, although we observed that the difference between the two tends to decrease as the degree of parallelism increases.