Approach: -Starting with Made-in-Europe mature software stack, and optimizing this stack to support energy-efficiency -Integrated software stack supporting task-based programming model -Computing on a commercial cutting-edge European-developed CPU–GPU–FPGA heterogeneous hardware substrate and FPGA-based Dataflow Engines (DFE) -Three use-cases (Smart home/city, AI, health) to test the integrated stack

1. The LEGaTO project has received funding from the European Union’s Horizon 2020
research and innovation programme under the grant agreement No 780681. www.legato-project.eu
Energy-aware Task Scheduling in LEGaTO:
Low Energy Toolset for Heterogeneous Computing
Miquel Pericàs (miquelp@chalmers.se)
Mustafa Abduljabbar (musabdu@chalmers.se)
Department of Computer Science and Engineering, Chalmers University of Technology
Project Goals
One order of
magnitude improvement
in energy-efficiency
for heterogeneous
hardware through the
use of energy
optimized-programming
model and runtime.
Starting with Made-in-Europe
mature software stack, and
optimizing this stack to
support energy-efficiency
Integrated software stack
supporting task-based
programming model
Computing on a commercial
cutting-edge European-developed
CPU–GPU–FPGA heterogeneous
hardware substrate and FPGA-
based Dataflow Engines (DFE)
Three use-cases (Smart
home/city, AI, health) to test
the integrated stack
Approach
5x decrease in Mean
Time to Failure
through energy-
efficient software-
based fault
tolerance.
Size reduction of the
trusted computing
base by at least one
order of magnitude.
5x times increase in
FPGA designer
productivity through
design of novel
features for hardware
design using dataflow
languages.
UseCases
Healthcare
Will demonstrate not
only a decrease in
energy consumption
but also an increase
in healthcare
application
resilience and
security.
Machine Learning
Will improve energy
efficiency by
employing
accelerators and
tuning the accuracy
of computations at
runtime using CNN
and LSTM.
IoT for smart
homes and cities
The LEGaTO project
software-hardware
framework for the IoT
will demonstrate ease
of programming and
energy savings in
smart homes and smart
cities applications.
Chalmers is extending the XiTAO runtime1
with a low-energy scheduler
XiTAO & Energy
Nvidia Jetson TX2:
2 Clusters (C0,C1)
C0: 2x Denver, 2MB L2
C1: 4x A57 , 2MB L2
FMIN
: 345 MHz
FMAX
: 2035 MHz
1
https://github.com/mpericas/xitao
●
Reduces Overheads
●
Reduces Parallel Slackness
●
Destructive Interference
●
Fine-grained parallelism
●
Overheads
●
Work-time Inflation
●
Improves Parallel Slackness
●
Bulk creation of parallelism
●
Interference-freedom
●
Constructive sharing
Energy of Energy-Aware Scheduler (EAS) vs Performance Scheduler and Random
Work Stealing Scheduler (RWSS) for DAG degrees of parallelism 2, 6 & 10, and Denver/A57 MIN/MAX frequency
0
10
20
30
40
50
60
10 6 2 10 6 2 10 6 2 10 6 2
MAX&MAX MAX&MIN MIN&MAX MIN&MIN
EnergyConsumption(mJ)
x10000
Energy Comparison of Matrix Multiplication
RWSS Perf-based EAS
0
5
10
15
20
25
10 6 2 10 6 2 10 6 2 10 6 2
MAX&MAX MAX&MIN MIN&MAX MIN&MIN
EnergyConsumption(mJ)
x10000
Energy Comparison of Copy
RWSS Perf-based EAS
0
10
20
30
40
50
60
10 6 2 10 6 2 10 6 2 10 6 2
MAX&MAX MAX&MIN MIN&MAX MIN&MIN
EnergyConsumption(mJ)
x10000
Energy Comparison of Stencil
RWSS Perf-based EAS