Energy-Aware Scheduling for High-Performance Computing Systems: A Survey

Kocot, Bartłomiej; Czarnul, Paweł; Proficz, Jerzy

doi:10.3390/en16020890

Cited by 10 publications

(3 citation statements)

References 130 publications

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“…There are special processors dedicated exclusively to algorithms that perform neural calculations [21]. Moreover, power consumption is also considered [22]. From an application point of view, the mode of the weight update during optimization is a key point for determining the role of the neural network in the algorithm cooperating with the electrical drive (Figure 2).…”

Section: Preliminaries and Short Description Of Methodologymentioning

confidence: 99%

Neural Network Applications in Electrical Drives—Trends in Control, Estimation, Diagnostics, and Construction

Kaminski

Tarczewski

2023

Energies

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Currently, applications of the algorithms based on artificial intelligence (AI) principles can be observed in various fields. This can be also noticed in the wide area of electrical drives. Consideration has been limited to neural networks; however, the tasks for the models can be defined as follows: control, state variable estimation, and diagnostics. In the subsequent sections of this paper, electrical machines, as well as power electronic devices, are assumed as the main objects. This paper describes the basics, issues, and possibilities related to the used tools and explains the growing popularity of neural network applications in automatic systems with electrical drives. The paper begins with the overall considerations; following that, the content proceeds with the details, and two specific examples are shown. The first example deals with a neural network-based speed controller tested in a structure with a synchronous reluctance motor. Then, the implementation of recurrent neural networks as state variable estimators is analyzed. The achieved results present a precise estimation of the load speed and the shaft torque signals from a two-mass system. All descriptions in the article are considered in the context of the trends and perspectives in modern algorithm applications for electrical drives.

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Section: Preliminaries and Short Description Of Methodologymentioning

confidence: 99%

Neural Network Applications in Electrical Drives—Trends in Control, Estimation, Diagnostics, and Construction

Kaminski

Tarczewski

2023

Energies

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“…Several power management methods have been proposed for multicore systems, ranging from heuristic algorithms to formal methods [15]. For example, Devaraj et al proposed a formal scheduler synthesis framework for safety-critical systems on multicores that guarantees adherence to a systemlevel peak power constraint while allowing optimal resource utilization [16].…”

Section: B Power Managementmentioning

confidence: 99%

Performance Improvement of Processor Through Configurable Approximate Arithmetic Units in Multicore Systems

Gharavi,

Safari

2024

IEEE Access

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Multicore systems are utilized in a wide range of applications, from embedded systems to highperformance applications. Controlling power consumption while maximizing performance under the Thermal Design Power (TDP) becomes increasingly important when power density emerges as the key restriction for multicore systems. Dynamic voltage-frequency scaling (DVFS) approaches have been effective in dynamically power control and are commercially accessible. We propose a novel approach to improve the performance of multicore systems by utilizing configurable approximate Arithmetic units. The proposed system includes a machine learning-based framework for online power regulation and quality monitoring of application output. This framework dynamically adjusts the frequency and precision of the Arithmetic units to maximize performance while considering TDP constraints and the desired output quality. The experimental results demonstrate the effectiveness of the proposed approach. Using a floating point approximate Arithmetic Logic Unit (ALU) with three distinct configurations in each core, the multicore system can execute approximable applications up to 19% faster than a precise multicore system, while operating within the same TDP limit. INDEX TERMSApproximate computing, Reconfigurable approximate design, Computer architecture, Machine learning.

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“…Additionally, contemporary high performance computing aims at consideration of energy and power, next to performance. 5 Specifically, of interest are power-aware schedulers for independent tasks in a heterogeneous cluster 6,7 or selection of devices under power caps in a heterogeneous CPU+GPU environment in order to minimize application execution time. 8 Energy-aware optimization has been demonstrated for various types of workloads and HPC systems, specifically for data-parallel applications.…”

Section: Introductionmentioning

confidence: 99%

A multithreaded CUDA and OpenMP based power‐aware programming framework for multi‐node GPU systems

Czarnul

2023

Concurrency and Computation

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SummaryIn the article, we have proposed a framework that allows programming a parallel application for a multi‐node system, with one or more graphical processing units (GPUs) per node, using an OpenMP+extended CUDA API. OpenMP is used for launching threads responsible for management of particular GPUs and extended CUDA calls allow to transfer data and launch kernels on local and remote GPUs. The framework hides inter‐node MPI communication from the programmer. For optimization, the implementation takes advantage of the MPI_THREAD_MULTIPLE mode allowing: multiple threads handling distinct GPUs as well as overlapping communication and computations transparently using multiple CUDA streams. The solution allows data parallelization across available GPUs in order to minimize execution time and supports a power‐aware mode in which GPUs are automatically selected for computations using a greedy approach in order not to exceed an imposed power limit. We have implemented and benchmarked three parallel applications including: finding the largest divisors; verification of the Collatz conjecture; finding patterns in vectors. These were tested on three various systems: a GPU cluster with 16 nodes, each with NVIDIA GTX 1060 GPU; a powerful 2‐node system—one node with 8 NVIDIA Quadro RTX 6000 GPUs, the second with 4 NVIDIA Quadro RTX 5000 GPUs; a heterogeneous environment with one node with 2 NVIDIA RTX 2080 and 2 nodes with NVIDIA GTX 1060 GPUs. We demonstrated effectiveness of the framework through execution times versus power caps within ranges of 100–1400 W, 250–3000 W, and 125–600 W for these systems respectively as well as gains from using two versus one CUDA streams per GPU. Finally, we have shown that for the testbed applications the solution allows to obtain high speed‐ups between 89.3% and 97.4% of the theoretically assessed ideal ones, for 16 nodes and 2 CUDA streams, demonstrating very good parallel efficiency.

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Energy-Aware Scheduling for High-Performance Computing Systems: A Survey

Cited by 10 publications

References 130 publications

Neural Network Applications in Electrical Drives—Trends in Control, Estimation, Diagnostics, and Construction

Neural Network Applications in Electrical Drives—Trends in Control, Estimation, Diagnostics, and Construction

Performance Improvement of Processor Through Configurable Approximate Arithmetic Units in Multicore Systems

A multithreaded CUDA and OpenMP based power‐aware programming framework for multi‐node GPU systems

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