scholarly journals Evaluation of Emerging Energy-Efficient Heterogeneous Computing Platforms for Biomolecular and Cellular Simulation Workloads

2016 ◽  
Author(s):  
John E. Stone ◽  
Michael J. Hallock ◽  
James C. Phillips ◽  
Joseph R. Peterson ◽  
Zaida Luthey-Schulten ◽  
...  
Keyword(s):  
Energy Efficient ◽  
Heterogeneous Computing ◽  
Computing Platforms

Energy-efficient scheduling for moldable real-time tasks on heterogeneous computing platforms

2017 ◽  
Vol 74 ◽  
pp. 46-60 ◽  
Author(s):  
Houssam-Eddine Zahaf ◽  
Abou El Hassen Benyamina ◽  
Richard Olejnik ◽  
Giuseppe Lipari
Keyword(s):  
Real Time ◽  
Energy Efficient ◽  
Heterogeneous Computing ◽  
Computing Platforms ◽  
Energy Efficient Scheduling

scholarly journals Scheduling Challenges in Mixed Critical Real-Time Heterogeneous Computing Platforms

2013 ◽  
Vol 18 ◽  
pp. 1891-1898
Author(s):  
Chetan Kumar N G ◽  
Sudhanshu Vyas ◽  
Ron K. Cytron ◽  
Christopher D. Gill ◽  
Joseph Zambreno ◽  
...  
Keyword(s):  
Real Time ◽  
Heterogeneous Computing ◽  
Computing Platforms

BlastFunction: A Full-stack Framework Bringing FPGA Hardware Acceleration to Cloud-native Applications

2022 ◽  
Vol 15 (2) ◽  
pp. 1-27
Author(s):  
Andrea Damiani ◽  
Giorgia Fiscaletti ◽  
Marco Bacis ◽  
Rolando Brondolin ◽  
Marco D. Santambrogio
Keyword(s):  
Heterogeneous Computing ◽  
Performance Metrics ◽  
State Of The Art ◽  
Hardware Acceleration ◽  
Standard Approach ◽  
Cloud Models ◽  
Experimental Campaign ◽  
Computing Platforms ◽  
Cloud Infrastructures

“Cloud-native” is the umbrella adjective describing the standard approach for developing applications that exploit cloud infrastructures’ scalability and elasticity at their best. As the application complexity and user-bases grow, designing for performance becomes a first-class engineering concern. As an answer to these needs, heterogeneous computing platforms gained widespread attention as powerful tools to continue meeting SLAs for compute-intensive cloud-native workloads. We propose BlastFunction, an FPGA-as-a-Service full-stack framework to ease FPGAs’ adoption for cloud-native workloads, integrating with the vast spectrum of fundamental cloud models. At the IaaS level, BlastFunction time-shares FPGA-based accelerators to provide multi-tenant access to accelerated resources without any code rewriting. At the PaaS level, BlastFunction accelerates functionalities leveraging the serverless model and scales functions proactively, depending on the workload’s performance. Further lowering the FPGAs’ adoption barrier, an accelerators’ registry hosts accelerated functions ready to be used within cloud-native applications, bringing the simplicity of a SaaS-like approach to the developers. After an extensive experimental campaign against state-of-the-art cloud scenarios, we show how BlastFunction leads to higher performance metrics (utilization and throughput) against native execution, with minimal latency and overhead differences. Moreover, the scaling scheme we propose outperforms the main serverless autoscaling algorithms in workload performance and scaling operation amount.

scholarly journals Performance and energy optimization of heterogeneous CPU-GPU systems for embedded applications

2021 ◽  
Author(s):  
Abdullah Siddiqui
Keyword(s):  
Embedded Systems ◽  
Power Consumption ◽  
Optimization Algorithm ◽  
Heterogeneous Computing ◽  
Energy Optimization ◽  
Systems Design ◽  
Application Partitioning ◽  
Computing Platforms ◽  
Embedded Applications ◽  
Software Partitioning

One of the most critical steps of embedded systems design is Hardware-Software partitioning. It is characterized by distributing the components of an application between hardware and software such that the user defined system constraints are satisfied. Heterogeneous computing platforms consisting of CPUs and GPUs have tremendous potential for enhancing the performance of embedded applications. The challenge of application partitioning for CPU-GPU mapping is much greater on such platforms due to their unique and diverse characteristics. In this thesis, an optimization algorithm is devised and presented for partitioning and mapping computational tasks on CPU-GPU platforms while keeping a check on the power consumption. Our methodology also uses parallelism in applications and their tasks by utilizing the architectural capabilities of the GPU. The optimization algorithm was tested with a MJPEG decoder, several benchmarks and synthetic graphs.

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