Automatic Offloading Research Articles

Study and evaluation of automatic offloading for function blocks of applications

Systems using graphical processing units (GPUs) and field-programmable gate arrays (FPGAs) have increased due to their advantages over central processing units (CPUs). However, such systems require the understanding of hardware-specific technical specifications such as Hardware Description Language (HDL) and compute unified device architecture (CUDA), which is a high hurdle. Based on this background, we previously proposed environment-adaptive software that enables automatic conversion, configuration and high-performance operation of existing code according to the hardware to be placed. As an element of this concept, we also proposed a method of automatically offloading loop statements of application source code for CPUs to GPUs and FPGAs. In this paper, we propose a method for offloading a function block, which is a larger unit, instead of individual loop statements in an application to achieve higher speed by automatically offloading to GPUs and FPGAs. We implemented the proposed method and evaluated it using current applications offloading to GPUs and FPGAs.

Study and evaluation of automatic offloading method in mixed offloading destination environment

Heterogeneous hardware other than a small-core central processing unit (CPU) such as a graphics processing unit (GPU), field-programmable gate array (FPGA), or multi-core CPU is increasingly being used. However, to use heterogeneous hardware, programmers must have sufficient technical skills to utilize OpenMP, CUDA, and OpenCL. On the basis of this, we have proposed an environment-adaptive software that enables automatic conversion, configuration, and high-performance operation of once written code, in accordance with the hardware to be placed. However, no techniques have been developed to properly and automatically offload applications in the mixed offloading destination environment such as GPU, FPGA, and multi-core CPU. In this paper, for a new element of environment-adaptive software, we study a method for offloading applications properly and automatically in an environment where the offloading destination is a mix of GPU, FPGA, and multi-core CPU. We evaluate the effectiveness of the proposed method in multiple applications.

Development and experimental validation of a system for agricultural grain unloading-on-the-go

This paper describes the development, simulation, and experimental validation of a novel grain unloading-on-the-go automation system (automatic offloading) for agricultural combine harvesters. Unloading-on-the-go is desirable during harvest, but it requires highly-skilled and exhausting labor because the combine operator must fulfill multiple tasks simultaneously. The automatic offloading system can unburden the combine operator by automatically monitoring the grain cart fill status, determining the appropriate auger location, and controlling the relative vehicle position and auger on/off. An automation architecture is proposed and experimentally demonstrated to automate the unloading-on-the-go process. To simulate the automatic offloading operation, a grain fill model and vehicle dynamics models were developed and validated with in-field testing. To allow for different operator-selected unloading scenarios, the automatic offloading controller has three fill strategies and two movement control options, “open-loop” and “closed-loop”. Simulation results demonstrated that both movement control options can achieve the fill target. The automatic offloading controller was implemented on a dSPACE MicroAutoBox II and integrated into a combine harvester. A PC-based user interface was developed for the combine operator to monitor unloading status and provide commands during the test. In addition, a stereo-camera-based perception system was connected to the automatic offloading controller via an Ethernet cable for grain fill profile measurement during unloading. In-field testing demonstrated that the automatic offloading system can effectively automate the unloading-on-the-go of a combine harvester to fill a grain cart to the desired level under nominal harvesting conditions. The achievable fill level for a 1000-bushel grain cart without spillage ranges from −0.7 m to −0.2 m for the near-edge grain height relative to the cart edge.

Automation of Agricultural Grain Unloading-on-the-go

This paper describes the development and experimental validation of a novel grain unloading-on-the-go automation system (automatic offloading) for agricultural combine harvesters. Unloading-on-the-go is desirable during harvest, but it requires highly-skilled and exhausting labor because the combine operator must fulfill multiple tasks simultaneously. The automatic offloading system can unburden the combine operator by automatically monitoring the grain cart fill status, determining the appropriate auger location, and controlling the relative vehicle position and auger on/of. An automation architecture is proposed and experimentally demonstrated to automate the unloading-on-the-go process. To allow for diferent operator-selected unloading scenarios, the automatic offloading controller has three fill strategies and two movement control options, “open-loop” and “closed-loop”. The automatic offloading controller was implemented on a dSPACE MicroAutoBox II and integrated into a combine harvester. In addition, a stereo-camera-based perception system was connected to the automatic offloading controller via an Ethernet cable for grain fill profile measurement during unloading. In-feld testing demonstrated that the automatic offloading system can effectively automate the unloading-on-the-go of a combine harvester to fill a grain cart to the desired level under nominal harvesting conditions.

Cost-optimized, data-protection-aware offloading between an edge data center and the cloud

An edge data center can host applications that require low-latency access to nearby end devices. If the resource requirements of the applications exceed the capacity of the edge data center, some non-latency-critical application components may be offloaded to the cloud. Such offloading may incur financial costs both for the use of cloud resources and for data transfer between the edge data center and the cloud. Moreover, such offloading may violate data protection requirements if components process sensitive data. The operator of the edge data center has to decide which components to keep in the edge data center and which ones to offload to the cloud. In this paper, we formalize this problem and prove that it is strongly NP-hard. We introduce an optimization algorithm that is fast enough to be run online for dynamic and automatic offloading decisions, guarantees that the solution satisfies hard constraints regarding latency, data protection, and capacity, and achieves near-optimal costs. We also show how the algorithm can be extended to handle multiple edge data centers. Experiments show that the cost of the solution found by our algorithm is on average only 2.7% higher than the optimum.

Study and evaluation of automatic GPU offloading method from various language applications

ABSTRACT Heterogeneous hardware other than a small-core central processing unit (CPU) is increasingly being used, such as a graphics processing unit (GPU), field-programmable gate array (FPGA) or many-core CPU. However, to use heterogeneous hardware, programmers must have sufficient technical skills to utilise OpenMP, CUDA, and OpenCL. On the basis of this, we previously proposed environment-adaptive software that enables automatic conversion, configuration, and high performance operation of once-written code, in accordance with the hardware to be placed. However, the source language for offloading was mainly C/C++ language applications, and there was no research into common offloading for various language applications. In this paper, for a new challenge, we study a common method for automatically offloading various language applications in not only C language but also Python and Java. We evaluate the effectiveness of the proposed method in multiple applications of various languages.

Automatic offloading method of loop statements of software to FPGA

With Moore's law coming to an end, using hardware other than central processing units (CPUs), such as more energy efficient field-programmable gate arrays (FPGAs), has recently been increasing. However, when using heterogeneous hardware other than CPUs, barriers of technical skills, such as in using Open Computing Language (OpenCL), are high. Therefore, I previously proposed environment-adaptive software that enables automatic conversion, configuration, and high-performance operation of once-written code according to the hardware to be placed. Offloading of some applications to graphics processing units (GPUs) was automated previously. In this paper, I proposed an automatic offloading method of appropriate target loop statements of applications as the first step in offloading to FPGAs. I evaluated the effectiveness of the proposed method by applied it to multiple applications.

Design and operation verification of an automated pressure mapping and modulating seat cushion for pressure ulcer prevention

A sensorized air cell-based seat cushion system was developed to address the issues of loading magnitude and duration at a sitting interface to aid in reducing risk of sitting acquired pressure ulcers. This system is capable of pressure mapping, redistribution, and offloading which were verified using an anthropomorphic model and a human subject. The system is comprised of an air cell array cushion, a pneumatic control unit, and a graphical user interface. ISO load deflection testing confirmed that the cushion's loading response is comparable to commercial air cell-based seat cushions. Testing demonstrated that the internal pressure of the air cells are indicative of interface pressure and can be used as input to pressure modulating algorithms. Uniform pressure distribution was achieved through automated pressure redistribution algorithm implementation where the immersion of a subject into the seat cushion increased and interface pressure decreased. High pressure point identification and automatic offloading were performed in which newly created high pressure points were addressed using subsequent redistribution. Pressure mapping enabled offloading and redistribution can objectively manage the effects of loading magnitude and duration at the sitting interface.

Automatic offloading of mobile applications into the cloud by means of genetic programming

The limited battery life of modern mobile devices is one of the key problems limiting their use. Even if the offloading of computation onto cloud computing platforms can considerably extend battery duration, it is really hard not only to evaluate the cases where offloading guarantees real advantages on the basis of the requirements of the application in terms of data transfer, computing power needed, etc., but also to evaluate whether user requirements (i.e. the costs of using the cloud services, a determined QoS required, etc.) are satisfied. To this aim, this paper presents a framework for generating models to make automatic decisions on the offloading of mobile applications using a genetic programming (GP) approach. The GP system is designed using a taxonomy of the properties useful to the offloading process concerning the user, the network, the data and the application. The fitness function adopted permits different weights to be given to the four categories considered during the process of building the model. Experimental results, conducted on datasets representing different categories of mobile applications, permit the analysis of the behavior of our algorithm in different applicative contexts. Finally, a comparison with the state of the art of the classification algorithm establishes the goodness of the approach in modeling the offloading process.