4,015 research outputs found
SARA: Self-Aware Resource Allocation for Heterogeneous MPSoCs
In modern heterogeneous MPSoCs, the management of shared memory resources is
crucial in delivering end-to-end QoS. Previous frameworks have either focused
on singular QoS targets or the allocation of partitionable resources among CPU
applications at relatively slow timescales. However, heterogeneous MPSoCs
typically require instant response from the memory system where most resources
cannot be partitioned. Moreover, the health of different cores in a
heterogeneous MPSoC is often measured by diverse performance objectives. In
this work, we propose a Self-Aware Resource Allocation (SARA) framework for
heterogeneous MPSoCs. Priority-based adaptation allows cores to use different
target performance and self-monitor their own intrinsic health. In response,
the system allocates non-partitionable resources based on priorities. The
proposed framework meets a diverse range of QoS demands from heterogeneous
cores.Comment: Accepted by the 55th annual Design Automation Conference 2018
(DAC'18
A Review on Software Architectures for Heterogeneous Platforms
The increasing demands for computing performance have been a reality
regardless of the requirements for smaller and more energy efficient devices.
Throughout the years, the strategy adopted by industry was to increase the
robustness of a single processor by increasing its clock frequency and mounting
more transistors so more calculations could be executed. However, it is known
that the physical limits of such processors are being reached, and one way to
fulfill such increasing computing demands has been to adopt a strategy based on
heterogeneous computing, i.e., using a heterogeneous platform containing more
than one type of processor. This way, different types of tasks can be executed
by processors that are specialized in them. Heterogeneous computing, however,
poses a number of challenges to software engineering, especially in the
architecture and deployment phases. In this paper, we conduct an empirical
study that aims at discovering the state-of-the-art in software architecture
for heterogeneous computing, with focus on deployment. We conduct a systematic
mapping study that retrieved 28 studies, which were critically assessed to
obtain an overview of the research field. We identified gaps and trends that
can be used by both researchers and practitioners as guides to further
investigate the topic
LEGaTO: first steps towards energy-efficient toolset for heterogeneous computing
LEGaTO is a three-year EU H2020 project which started in December 2017. The LEGaTO project will leverage task-based programming models to provide a software ecosystem for Made-in-Europe heterogeneous hardware composed of CPUs, GPUs, FPGAs and dataflow engines. The aim is to attain one order of magnitude energy savings from the edge to the converged cloud/HPC.Peer ReviewedPostprint (author's final draft
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