3 research outputs found
Energy-Efficient Multiprocessor Scheduling for Flow Time and Makespan
We consider energy-efficient scheduling on multiprocessors, where the speed
of each processor can be individually scaled, and a processor consumes power
when running at speed , for . A scheduling algorithm
needs to decide at any time both processor allocations and processor speeds for
a set of parallel jobs with time-varying parallelism. The objective is to
minimize the sum of the total energy consumption and certain performance
metric, which in this paper includes total flow time and makespan. For both
objectives, we present instantaneous parallelism clairvoyant (IP-clairvoyant)
algorithms that are aware of the instantaneous parallelism of the jobs at any
time but not their future characteristics, such as remaining parallelism and
work. For total flow time plus energy, we present an -competitive
algorithm, which significantly improves upon the best known non-clairvoyant
algorithm and is the first constant competitive result on multiprocessor speed
scaling for parallel jobs. In the case of makespan plus energy, which is
considered for the first time in the literature, we present an
-competitive algorithm, where is the total number of
processors. We show that this algorithm is asymptotically optimal by providing
a matching lower bound. In addition, we also study non-clairvoyant scheduling
for total flow time plus energy, and present an algorithm that achieves -competitive for jobs with arbitrary release time and
-competitive for jobs with identical release time. Finally,
we prove an lower bound on the competitive ratio of
any non-clairvoyant algorithm, matching the upper bound of our algorithm for
jobs with identical release time
Fewer Cores, More Hertz: Leveraging High-Frequency Cores in the OS Scheduler for Improved Application Performance
International audienceIn modern server CPUs, individual cores can run at different frequencies, which allows for fine-grained control of the per-formance/energy tradeoff. Adjusting the frequency, however, incurs a high latency. We find that this can lead to a problem of frequency inversion, whereby the Linux scheduler places a newly active thread on an idle core that takes dozens to hundreds of milliseconds to reach a high frequency, just before another core already running at a high frequency becomes idle. In this paper, we first illustrate the significant performance overhead of repeated frequency inversion through a case study of scheduler behavior during the compilation of the Linux kernel on an 80-core Intel R Xeon-based machine. Following this, we propose two strategies to reduce the likelihood of frequency inversion in the Linux scheduler. When benchmarked over 60 diverse applications on the Intel R Xeon, the better performing strategy, S move , improves performance by more than 5% (at most 56% with no energy overhead) for 23 applications, and worsens performance by more than 5% (at most 8%) for only 3 applications. On a 4-core AMD Ryzen we obtain performance improvements up to 56%
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A Dynamic Reconfiguration Framework to Maximize Performance/Power in Asymmetric Multicore Processors
Recent trends in technology scaling have shifted the processing paradigm to multicores. Depending on the characteristics of the cores, the multicores can be either symmetric or asymmetric. Prior research has shown that Asymmetric Multicore Processors (AMPs) outperform their symmetric (SMP) counterparts within a given resource and power budget. But, due to the heterogeneity in core-types and time-varying workload behavior, thread-to-core assignment is always a challenge in AMPs. As the computational requirements vary significantly across different applications and with time, there is a need to dynamically allocate appropriate computational resources on demand to suit the applicationsā current needs, in order to maximize the performance and minimize the energy consumption. Performance/power of the applications could be further increased by dynamically adapting the voltage and frequency of the cores to better fit the changing characteristics of the workloads. Not only can a core be forced to a low power mode when its activity level is low, but the power saved by doing so could be opportunistically re-budgeted to the other cores to boost the overall system throughput.
To this end, we propose a novel solution that seamlessly combines heterogeneity with a Dynamic Reconfiguration Framework (DRF). The proposed dynamic reconfiguration framework is equipped with Dynamic Resource Allocation (DRA) and Voltage/Frequency Adaptation (DVFA) capabilities to adapt the core resources and operating conditions at runtime to the changing demands of the applications. As a proof of concept, we illustrate our proposed approach using a dual-core AMP and demonstrate significant performance/power benefits over various baselines