EbbRT: Elastic Building Block Runtime - overview

EbbRT provides a lightweight runtime that enables the construction of reusable, low-level system software which can integrate with existing, general purpose systems. It achieves this by providing a library that can be linked into a process on an existing OS, and as a small library OS that can be booted directly on an IaaS node

A Generic Communications Module for Cooperative 3D Visualization and Modelling over the Internet: the Collaborative API

Cooperative three-dimensional visualization and modeling applications allow a distributed group of users to work together with a model they share. To implement this kind of applications the underlying communications system must provide reliable and ordered multicast of users interactions. Due to the high complexity that characterizes the models, network bandwidth requirements have limited their use to intranets or in a few cases to very high-speed Internet connections. In this paper we present a communications module that solves this problem. The library exposed, which is called Collaborative API, supports the creation of very efficient cooperative 3D visualization and modeling applications by optimizing the use of the network resources. The Collaborative API, implements a new communications architecture: the dynamic client/server. The communications module presented in this paper is illustrated by two examples of applications that use it to provide cooperative 3D visualization over the Internet

PyCARL: A PyNN Interface for Hardware-Software Co-Simulation of Spiking Neural Network

We present PyCARL, a PyNN-based common Python programming interface for hardware-software co-simulation of spiking neural network (SNN). Through PyCARL, we make the following two key contributions. First, we provide an interface of PyNN to CARLsim, a computationally-efficient, GPU-accelerated and biophysically-detailed SNN simulator. PyCARL facilitates joint development of machine learning models and code sharing between CARLsim and PyNN users, promoting an integrated and larger neuromorphic community. Second, we integrate cycle-accurate models of state-of-the-art neuromorphic hardware such as TrueNorth, Loihi, and DynapSE in PyCARL, to accurately model hardware latencies that delay spikes between communicating neurons and degrade performance. PyCARL allows users to analyze and optimize the performance difference between software-only simulation and hardware-software co-simulation of their machine learning models. We show that system designers can also use PyCARL to perform design-space exploration early in the product development stage, facilitating faster time-to-deployment of neuromorphic products. We evaluate the memory usage and simulation time of PyCARL using functionality tests, synthetic SNNs, and realistic applications. Our results demonstrate that for large SNNs, PyCARL does not lead to any significant overhead compared to CARLsim. We also use PyCARL to analyze these SNNs for a state-of-the-art neuromorphic hardware and demonstrate a significant performance deviation from software-only simulations. PyCARL allows to evaluate and minimize such differences early during model development.Comment: 10 pages, 25 figures. Accepted for publication at International Joint Conference on Neural Networks (IJCNN) 202

EbbRT: Elastic Building Block Runtime - case studies

We present a new systems runtime, EbbRT, for cloud hosted applications. EbbRT takes a different approach to the role operating systems play in cloud computing. It supports stitching application functionality across nodes running commodity OSs and nodes running specialized application specific software that only execute what is necessary to accelerate core functions of the application. In doing so, it allows tradeoffs between efficiency, developer productivity, and exploitation of elasticity and scale. EbbRT, as a software model, is a framework for constructing applications as collections of standard application software and Elastic Building Blocks (Ebbs). Elastic Building Blocks are components that encapsulate runtime software objects and are implemented to exploit the raw access, scale and elasticity of IaaS resources to accelerate critical application functionality. This paper presents the EbbRT architecture, our prototype and experimental evaluation of the prototype under three different application scenarios

Many-Task Computing and Blue Waters

This report discusses many-task computing (MTC) generically and in the context of the proposed Blue Waters systems, which is planned to be the largest NSF-funded supercomputer when it begins production use in 2012. The aim of this report is to inform the BW project about MTC, including understanding aspects of MTC applications that can be used to characterize the domain and understanding the implications of these aspects to middleware and policies. Many MTC applications do not neatly fit the stereotypes of high-performance computing (HPC) or high-throughput computing (HTC) applications. Like HTC applications, by definition MTC applications are structured as graphs of discrete tasks, with explicit input and output dependencies forming the graph edges. However, MTC applications have significant features that distinguish them from typical HTC applications. In particular, different engineering constraints for hardware and software must be met in order to support these applications. HTC applications have traditionally run on platforms such as grids and clusters, through either workflow systems or parallel programming systems. MTC applications, in contrast, will often demand a short time to solution, may be communication intensive or data intensive, and may comprise very short tasks. Therefore, hardware and software for MTC must be engineered to support the additional communication and I/O and must minimize task dispatch overheads. The hardware of large-scale HPC systems, with its high degree of parallelism and support for intensive communication, is well suited for MTC applications. However, HPC systems often lack a dynamic resource-provisioning feature, are not ideal for task communication via the file system, and have an I/O system that is not optimized for MTC-style applications. Hence, additional software support is likely to be required to gain full benefit from the HPC hardware

Technologie RFID a Blochkchain v dodavatelském řetězci

The paper discusses the possibility of combining RFID and Blockchain technology to more effectively prevent counterfeiting of products or raw materials, and to solve problems related to production, logistics and storage. Linking these technologies can lead to better planning by increasing the transparency and traceability of industrial or logistical processes or such as efficient detection of critical chain sites.Příspěvek se zabývá možností kombinace technologií RFID a Blockchain pro účinnější zabránění padělání výrobků či surovin a řešení problémů spojených s výrobou, logistikou a skladováním. Spojení těchto technologií může vést k lepšímu plánování díky vyšší transparentnosti a sledovatelnosti průmyslových nebo logistických procesů, nebo například k efektivnímu zjišťování kritických míst řetězce

The pros and cons of using SDL for creation of distributed services

In a competitive market for the creation of complex distributed services, time to market, development cost, maintenance and flexibility are key issues. Optimizing the development process is very much a matter of optimizing the technologies used during service creation. This paper reports on the experience gained in the Service Creation projects SCREEN and TOSCA on use of the language SDL for efficient service creation