Reclaiming the energy of a schedule: models and algorithms

We consider a task graph to be executed on a set of processors. We assume that the mapping is given, say by an ordered list of tasks to execute on each processor, and we aim at optimizing the energy consumption while enforcing a prescribed bound on the execution time. While it is not possible to change the allocation of a task, it is possible to change its speed. Rather than using a local approach such as backfilling, we consider the problem as a whole and study the impact of several speed variation models on its complexity. For continuous speeds, we give a closed-form formula for trees and series-parallel graphs, and we cast the problem into a geometric programming problem for general directed acyclic graphs. We show that the classical dynamic voltage and frequency scaling (DVFS) model with discrete modes leads to a NP-complete problem, even if the modes are regularly distributed (an important particular case in practice, which we analyze as the incremental model). On the contrary, the VDD-hopping model leads to a polynomial solution. Finally, we provide an approximation algorithm for the incremental model, which we extend for the general DVFS model.Comment: A two-page extended abstract of this work appeared as a short presentation in SPAA'2011, while the long version has been accepted for publication in "Concurrency and Computation: Practice and Experience

Evaluation of DVFS techniques on modern HPC processors and accelerators for energy-aware applications

Energy efficiency is becoming increasingly important for computing systems, in particular for large scale HPC facilities. In this work we evaluate, from an user perspective, the use of Dynamic Voltage and Frequency Scaling (DVFS) techniques, assisted by the power and energy monitoring capabilities of modern processors in order to tune applications for energy efficiency. We run selected kernels and a full HPC application on two high-end processors widely used in the HPC context, namely an NVIDIA K80 GPU and an Intel Haswell CPU. We evaluate the available trade-offs between energy-to-solution and time-to-solution, attempting a function-by-function frequency tuning. We finally estimate the benefits obtainable running the full code on a HPC multi-GPU node, with respect to default clock frequency governors. We instrument our code to accurately monitor power consumption and execution time without the need of any additional hardware, and we enable it to change CPUs and GPUs clock frequencies while running. We analyze our results on the different architectures using a simple energy-performance model, and derive a number of energy saving strategies which can be easily adopted on recent high-end HPC systems for generic applications

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

Optimizing energy efficiency of CNN-based object detection with dynamic voltage and frequency scaling

On the one hand, accelerating convolution neural networks (CNNs) on FPGAs requires ever increasing high energy efficiency in the edge computing paradigm. On the other hand, unlike normal digital algorithms, CNNs maintain their high robustness even with limited timing errors. By taking advantage of this unique feature, we propose to use dynamic voltage and frequency scaling (DVFS) to further optimize the energy efficiency for CNNs. First, we have developed a DVFS framework on FPGAs. Second, we apply the DVFS to SkyNet, a state-of-the-art neural network targeting on object detection. Third, we analyze the impact of DVFS on CNNs in terms of performance, power, energy efficiency and accuracy. Compared to the state-of-the-art, experimental results show that we have achieved 38% improvement in energy efficiency without any loss in accuracy. Results also show that we can achieve 47% improvement in energy efficiency if we allow 0.11% relaxation in accuracy