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Performance-, power-, and energy-aware scheduling techniques play an essential role in optimally utilizing processing elements (PEs) of heterogeneous systems. List schedulers, a class of low-complexity static schedulers, have commonly been…

Distributed, Parallel, and Cluster Computing · Computer Science 2021-12-17 Joshua Mack , Samet E. Arda , Umit Y. Ogras , Ali Akoglu

In latency-sensitive applications, efficient task scheduling is crucial for maintaining Quality of Service (QoS) while meeting strict timing constraints. This paper addresses the challenge of scheduling periodic tasks structured as directed…

Distributed, Parallel, and Cluster Computing · Computer Science 2025-06-17 Ashutosh Shankar , Astha Kumari

In recent processor development, we have witnessed the integration of GPU and CPUs into a single chip. The result of this integration is a reduction of the data communication overheads. This enables an efficient collaboration of both…

Distributed, Parallel, and Cluster Computing · Computer Science 2016-08-07 Francisco Corbera , Andrés Rodríguez , Rafael Asenjo , Angeles Navarro , Antonio Vilches , María J. Garzarán

The emergence of new, off-path smart network cards (SmartNICs), known generally as Data Processing Units (DPU), has opened a wide range of research opportunities. Of particular interest is the use of these and related devices in tandem with…

Distributed, Parallel, and Cluster Computing · Computer Science 2025-12-04 Nathan Tibbetts , Sifat Ibtisum , Satish Puri

Multiprocessor scheduling of hard real-time tasks modeled by directed acyclic graphs (DAGs) exploits the inherent parallelism presented by the model. For DAG tasks, a node represents a request to execute an object on one of the available…

Distributed, Parallel, and Cluster Computing · Computer Science 2020-03-02 Corey Tessler , Venkata P. Modekurthy , Nathan Fisher , Abusayeed Saifullah

The advent of heterogeneous multi-core architectures brought with it huge benefits to energy efficiency by running programs on properly-sized cores. Modern heterogeneous multi-core systems as suggested by Artjom et al. schedule tasks to…

Hardware Architecture · Computer Science 2023-06-28 Alok Anand , Ivan Khokhlov , Abhishek Anand

Modern commodity computing systems are composed by a number of different heterogeneous processing units, each of which has its own unique performance and energy characteristics. However, the majority of current network packet processing…

Networking and Internet Architecture · Computer Science 2022-05-02 Giannis Giakoumakis , Eva Papadogiannaki , Giorgos Vasiliadis , Sotiris Ioannidis

In the past decade, high performance compute capabilities exhibited by heterogeneous GPGPU platforms have led to the popularity of data parallel programming languages such as CUDA and OpenCL. Such languages, however, involve a steep…

Distributed, Parallel, and Cluster Computing · Computer Science 2020-09-17 Anirban Ghose , Siddharth Singh , Vivek Kulaharia , Lokesh Dokara , Srijeeta Maity , Soumyajit Dey

Supercomputer architectures are trending toward higher computational throughput due to the inclusion of heterogeneous compute nodes. These multi-GPU nodes increase on-node computational efficiency, while also increasing the amount of data…

Distributed, Parallel, and Cluster Computing · Computer Science 2022-09-14 Shelby Lockhart , Amanda Bienz , William D. Gropp , Luke N. Olson

As modern HPC computing platforms become increasingly heterogeneous, it is challenging for programmers to fully leverage the computation power of massive parallelism offered by such heterogeneity. Consequently, task-based runtime systems…

Distributed, Parallel, and Cluster Computing · Computer Science 2024-04-05 Yiqing Wang , Xiaoyan Liu , Hailong Yang , Xinyu Yang , Pengbo Wang , Yi Liu , Zhongzhi Luan , Depei Qian

Heterogeneous computing systems, which combine general-purpose processors with specialized accelerators, are increasingly important for optimizing the performance of modern applications. A central challenge is to decide which parts of an…

Distributed, Parallel, and Cluster Computing · Computer Science 2026-04-15 Martin Wilhelm , Franz Freitag , Max Tzschoppe , Thilo Pionteck

CPU-GPU heterogeneous architectures are now commonly used in a wide variety of computing systems from mobile devices to supercomputers. Maximizing the throughput for multi-programmed workloads on such systems is indispensable as one single…

Distributed, Parallel, and Cluster Computing · Computer Science 2024-05-08 Issa Saba , Eishi Arima , Dai Liu , Martin Schulz

As multicore hardware is becoming increasingly common in real-time systems, traditional scheduling techniques that assume a single worst-case execution time for a task are no longer adequate, since they ignore the impact of shared resources…

Operating Systems · Computer Science 2025-01-16 Robert Gifford , Abby Eisenklam , Georgiy A. Bondar , Yifan Cai , Tushar Sial , Linh Thi Xuan Phan , Abhishek Halder

Efficient runtime task scheduling on complex memory hierarchy becomes increasingly important as modern and future High-Performance Computing (HPC) systems are progressively composed of multisocket and multi-chiplet nodes with nonuniform…

Distributed, Parallel, and Cluster Computing · Computer Science 2021-12-20 Mustafa Abduljabbar , Mahmoud Eljammaly , Miquel Pericas

We study the problem of scheduling a general computational DAG on multiple processors in a 2-level memory hierarchy. This setting is a natural generalization of several prominent models in the literature, and it simultaneously captures…

Distributed, Parallel, and Cluster Computing · Computer Science 2025-07-24 Pál András Papp , Toni Böhnlein , A. N. Yzelman

The cost of data movement on parallel systems varies greatly with machine architecture, job partition, and nearby jobs. Performance models that accurately capture the cost of data movement provide a tool for analysis, allowing for…

Distributed, Parallel, and Cluster Computing · Computer Science 2021-07-20 Amanda Bienz , Luke N. Olson , William D. Gropp , Shelby Lockhart

Specialized accelerators such as GPUs, TPUs, FPGAs, and custom ASICs have been increasingly deployed to train deep learning models. These accelerators exhibit heterogeneous performance behavior across model architectures. Existing…

Distributed, Parallel, and Cluster Computing · Computer Science 2020-08-24 Deepak Narayanan , Keshav Santhanam , Fiodar Kazhamiaka , Amar Phanishayee , Matei Zaharia

This paper presents a systematic review of mapping and scheduling strategies within the High-Performance Computing (HPC) compute continuum, with a particular emphasis on heterogeneous systems. It introduces a prototype workflow to establish…

Distributed, Parallel, and Cluster Computing · Computer Science 2025-05-19 Aasish Kumar Sharma , Julian Kunkel

The rapid development of the mobile Internet and the Internet of Things is leading to a diversification of user devices and the emergence of new mobile applications on a regular basis. Such applications include those that are…

Computational Engineering, Finance, and Science · Computer Science 2024-08-13 Xirui Tang , Zeyu Wang , Xiaowei Cai , Honghua Su , Changsong Wei

Traditionally, on-demand, rigid, and malleable applications have been scheduled and executed on separate systems. The ever-growing workload demands and rapidly developing HPC infrastructure trigger the interest of converging these…

Distributed, Parallel, and Cluster Computing · Computer Science 2021-09-14 Yuping Fan , Paul Rich , William Allcock , Michael Papka , Zhiling Lan