High-Performance Computing (HPC) processors are nowadays integrated Cyber-Physical Systems requiring complex and high-performance closed-loop control strategies for efficient power and thermal management. To satisfy high-bandwidth, real-time multi-input multi-output (MIMO) optimal power control requirements, high-end processors integrate on-die Power Controller Systems (PCS). Traditional PCS is based on a simple microcontroller core supported by dedicated interface logic and sequencers. More scalable and flexible PCS architectures are required to support advanced MIMO control algorithms required for managing the ever-increasing number of cores, power states, and process, voltage, temperature (PVT) variability. In this paper, we present ControlPULP, a complete, open-source HW/SW RISC-V parallel PCS platform consisting of a single-core microcontroller coupled with a scalable multi-core cluster system with a specialized DMA engine and a fast multi-core interrupt controller for parallel acceleration of real-time power management policies. ControlPULP relies on a real-time OS (FreeRTOS) to schedule a Power Control Firmware (PCF) software layer. We evaluate ControlPULP design choices in a cycle-accurate, event-based simulation environment and show the benefits of the proposed multi-core acceleration solution. We demonstrate ControlPULP in a PCS use-case targeting a next-generation 72-cores HPC processor. We show that the multi-core cluster accelerates the PCF achieving 4.9x speedup with respect to single-core execution.

Ottaviano A., Balas R., Bambini G., Bonfanti C., Benatti S., Rossi D., et al. (2022). ControlPULP: A RISC-V Power Controller for HPC Processors with Parallel Control-Law Computation Acceleration. GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND : Springer Science and Business Media Deutschland GmbH [10.1007/978-3-031-15074-6_8].

ControlPULP: A RISC-V Power Controller for HPC Processors with Parallel Control-Law Computation Acceleration

Bambini G.;Bonfanti C.;Rossi D.;Benini L.;Bartolini A.
Ultimo
2022

Abstract

High-Performance Computing (HPC) processors are nowadays integrated Cyber-Physical Systems requiring complex and high-performance closed-loop control strategies for efficient power and thermal management. To satisfy high-bandwidth, real-time multi-input multi-output (MIMO) optimal power control requirements, high-end processors integrate on-die Power Controller Systems (PCS). Traditional PCS is based on a simple microcontroller core supported by dedicated interface logic and sequencers. More scalable and flexible PCS architectures are required to support advanced MIMO control algorithms required for managing the ever-increasing number of cores, power states, and process, voltage, temperature (PVT) variability. In this paper, we present ControlPULP, a complete, open-source HW/SW RISC-V parallel PCS platform consisting of a single-core microcontroller coupled with a scalable multi-core cluster system with a specialized DMA engine and a fast multi-core interrupt controller for parallel acceleration of real-time power management policies. ControlPULP relies on a real-time OS (FreeRTOS) to schedule a Power Control Firmware (PCF) software layer. We evaluate ControlPULP design choices in a cycle-accurate, event-based simulation environment and show the benefits of the proposed multi-core acceleration solution. We demonstrate ControlPULP in a PCS use-case targeting a next-generation 72-cores HPC processor. We show that the multi-core cluster accelerates the PCF achieving 4.9x speedup with respect to single-core execution.
2022
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
120
135
Ottaviano A., Balas R., Bambini G., Bonfanti C., Benatti S., Rossi D., et al. (2022). ControlPULP: A RISC-V Power Controller for HPC Processors with Parallel Control-Law Computation Acceleration. GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND : Springer Science and Business Media Deutschland GmbH [10.1007/978-3-031-15074-6_8].
Ottaviano A.; Balas R.; Bambini G.; Bonfanti C.; Benatti S.; Rossi D.; Benini L.; Bartolini A.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/905849
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