2,646 research outputs found
Efficient Neural Network Implementations on Parallel Embedded Platforms Applied to Real-Time Torque-Vectoring Optimization Using Predictions for Multi-Motor Electric Vehicles
The combination of machine learning and heterogeneous embedded platforms enables new potential for developing sophisticated control concepts which are applicable to the field of vehicle dynamics and ADAS. This interdisciplinary work provides enabler solutions -ultimately implementing fast predictions using neural networks (NNs) on field programmable gate arrays (FPGAs) and graphical processing units (GPUs)- while applying them to a challenging application: Torque Vectoring on a multi-electric-motor vehicle for enhanced vehicle dynamics. The foundation motivating this work is provided by discussing multiple domains of the technological context as well as the constraints related to the automotive field, which contrast with the attractiveness of exploiting the capabilities of new embedded platforms to apply advanced control algorithms for complex control problems. In this particular case we target enhanced vehicle dynamics on a multi-motor electric vehicle benefiting from the greater degrees of freedom and controllability offered by such powertrains. Considering the constraints of the application and the implications of the selected multivariable optimization challenge, we propose a NN to provide batch predictions for real-time optimization. This leads to the major contribution of this work: efficient NN implementations on two intrinsically parallel embedded platforms, a GPU and a FPGA, following an analysis of theoretical and practical implications of their different operating paradigms, in order to efficiently harness their computing potential while gaining insight into their peculiarities. The achieved results exceed the expectations and additionally provide a representative illustration of the strengths and weaknesses of each kind of platform. Consequently, having shown the applicability of the proposed solutions, this work contributes valuable enablers also for further developments following similar fundamental principles.Some of the results presented in this work are related to activities within the 3Ccar project, which has
received funding from ECSEL Joint Undertaking under grant agreement No. 662192. This Joint Undertaking
received support from the European Union’s Horizon 2020 research and innovation programme and Germany,
Austria, Czech Republic, Romania, Belgium, United Kingdom, France, Netherlands, Latvia, Finland, Spain, Italy,
Lithuania. This work was also partly supported by the project ENABLES3, which received funding from ECSEL
Joint Undertaking under grant agreement No. 692455-2
Best bang for your buck: GPU nodes for GROMACS biomolecular simulations
The molecular dynamics simulation package GROMACS runs efficiently on a wide
variety of hardware from commodity workstations to high performance computing
clusters. Hardware features are well exploited with a combination of SIMD,
multi-threading, and MPI-based SPMD/MPMD parallelism, while GPUs can be used as
accelerators to compute interactions offloaded from the CPU. Here we evaluate
which hardware produces trajectories with GROMACS 4.6 or 5.0 in the most
economical way. We have assembled and benchmarked compute nodes with various
CPU/GPU combinations to identify optimal compositions in terms of raw
trajectory production rate, performance-to-price ratio, energy efficiency, and
several other criteria. Though hardware prices are naturally subject to trends
and fluctuations, general tendencies are clearly visible. Adding any type of
GPU significantly boosts a node's simulation performance. For inexpensive
consumer-class GPUs this improvement equally reflects in the
performance-to-price ratio. Although memory issues in consumer-class GPUs could
pass unnoticed since these cards do not support ECC memory, unreliable GPUs can
be sorted out with memory checking tools. Apart from the obvious determinants
for cost-efficiency like hardware expenses and raw performance, the energy
consumption of a node is a major cost factor. Over the typical hardware
lifetime until replacement of a few years, the costs for electrical power and
cooling can become larger than the costs of the hardware itself. Taking that
into account, nodes with a well-balanced ratio of CPU and consumer-class GPU
resources produce the maximum amount of GROMACS trajectory over their lifetime
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
CLBlast: A Tuned OpenCL BLAS Library
This work introduces CLBlast, an open-source BLAS library providing optimized
OpenCL routines to accelerate dense linear algebra for a wide variety of
devices. It is targeted at machine learning and HPC applications and thus
provides a fast matrix-multiplication routine (GEMM) to accelerate the core of
many applications (e.g. deep learning, iterative solvers, astrophysics,
computational fluid dynamics, quantum chemistry). CLBlast has five main
advantages over other OpenCL BLAS libraries: 1) it is optimized for and tested
on a large variety of OpenCL devices including less commonly used devices such
as embedded and low-power GPUs, 2) it can be explicitly tuned for specific
problem-sizes on specific hardware platforms, 3) it can perform operations in
half-precision floating-point FP16 saving bandwidth, time and energy, 4) it has
an optional CUDA back-end, 5) and it can combine multiple operations in a
single batched routine, accelerating smaller problems significantly. This paper
describes the library and demonstrates the advantages of CLBlast experimentally
for different use-cases on a wide variety of OpenCL hardware.Comment: Conference paper in: IWOCL '18, the International Workshop on OpenC
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