activity
20192022
most citedERASE: Energy Efficient Task Mapping and Resource Management for Work Stealing Runtimes

15 citations · 17 across the 6 of their papers we have counts for

collaborators

6 papers

cs.DC202215 cited

ERASE: Energy Efficient Task Mapping and Resource Management for Work Stealing Runtimes

Jing Chen, Madhavan Manivannan, Mustafa Abduljabbar +1

Parallel applications often rely on work stealing schedulers in combination with fine-grained tasking to achieve high performance and scalability. However, reducing the total energ…

cs.AR2021

CBP: Coordinated management of cache partitioning, bandwidth partitioning and prefetch throttling

Nadja Ramhöj Holtryd, Madhavan Manivannan, Per Stenström +1

Reducing the average memory access time is crucial for improving the performance of applications running on multi-core architectures. With workload consolidation this becomes incre…

cs.DC2020

Scheduling Task-parallel Applications in Dynamically Asymmetric Environments

Jing Chen, Pirah Noor Soomro, Mustafa Abduljabbar +2

Shared resource interference is observed by applications as dynamic performance asymmetry. Prior art has developed approaches to reduce the impact of performance asymmetry mainly a…

cs.DC20192 cited

LEGaTO: Low-Energy, Secure, and Resilient Toolset for Heterogeneous Computing

B. Salami, K. Parasyris, A. Cristal +42

The LEGaTO project leverages task-based programming models to provide a software ecosystem for Made in-Europe heterogeneous hardware composed of CPUs, GPUs, FPGAs and dataflow engi…

cs.AR2019

Coordinated Management of Processor Configuration and Cache Partitioning to Optimize Energy under QoS Constraints

Mehrzad Nejat, Madhavan Manivannan, Miquel Pericas +1

An effective way to improve energy efficiency is to throttle hardware resources to meet a certain performance target, specified as a QoS constraint, associated with all application…

cs.AR2019

Coordinated Management of DVFS and Cache Partitioning under QoS Constraints to Save Energy in Multi-Core Systems

Mehrzad Nejat, Madhavan Manivannan, Miquel Pericas +1

Reducing the energy expended to carry out a computational task is important. In this work, we explore the prospects of meeting Quality-of-Service requirements of tasks on a multi-c…