9,595 research outputs found
Building real-time embedded applications on QduinoMC: a web-connected 3D printer case study
Single Board Computers (SBCs) are now emerging
with multiple cores, ADCs, GPIOs, PWM channels, integrated
graphics, and several serial bus interfaces. The low power
consumption, small form factor and I/O interface capabilities of
SBCs with sensors and actuators makes them ideal in embedded
and real-time applications. However, most SBCs run non-realtime
operating systems based on Linux and Windows, and do
not provide a user-friendly API for application development. This
paper presents QduinoMC, a multicore extension to the popular
Arduino programming environment, which runs on the Quest
real-time operating system. QduinoMC is an extension of our earlier
single-core, real-time, multithreaded Qduino API. We show
the utility of QduinoMC by applying it to a specific application: a
web-connected 3D printer. This differs from existing 3D printers,
which run relatively simple firmware and lack operating system
support to spool multiple jobs, or interoperate with other devices
(e.g., in a print farm). We show how QduinoMC empowers devices with the capabilities to run new services without impacting their timing guarantees. While it is possible to modify existing operating systems to provide suitable timing guarantees, the effort to do so is cumbersome and does not provide the ease of programming afforded by QduinoMC.http://www.cs.bu.edu/fac/richwest/papers/rtas_2017.pdfAccepted manuscrip
TechNews digests: Jan - Nov 2009
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Open Source Software: From Open Science to New Marketing Models
-Open source Software; Intellectual Property; Licensing; Business Model.
The Dark Energy Survey Data Management System
The Dark Energy Survey collaboration will study cosmic acceleration with a
5000 deg2 griZY survey in the southern sky over 525 nights from 2011-2016. The
DES data management (DESDM) system will be used to process and archive these
data and the resulting science ready data products. The DESDM system consists
of an integrated archive, a processing framework, an ensemble of astronomy
codes and a data access framework. We are developing the DESDM system for
operation in the high performance computing (HPC) environments at NCSA and
Fermilab. Operating the DESDM system in an HPC environment offers both speed
and flexibility. We will employ it for our regular nightly processing needs,
and for more compute-intensive tasks such as large scale image coaddition
campaigns, extraction of weak lensing shear from the full survey dataset, and
massive seasonal reprocessing of the DES data. Data products will be available
to the Collaboration and later to the public through a virtual-observatory
compatible web portal. Our approach leverages investments in publicly available
HPC systems, greatly reducing hardware and maintenance costs to the project,
which must deploy and maintain only the storage, database platforms and
orchestration and web portal nodes that are specific to DESDM. In Fall 2007, we
tested the current DESDM system on both simulated and real survey data. We used
Teragrid to process 10 simulated DES nights (3TB of raw data), ingesting and
calibrating approximately 250 million objects into the DES Archive database. We
also used DESDM to process and calibrate over 50 nights of survey data acquired
with the Mosaic2 camera. Comparison to truth tables in the case of the
simulated data and internal crosschecks in the case of the real data indicate
that astrometric and photometric data quality is excellent.Comment: To be published in the proceedings of the SPIE conference on
Astronomical Instrumentation (held in Marseille in June 2008). This preprint
is made available with the permission of SPIE. Further information together
with preprint containing full quality images is available at
http://desweb.cosmology.uiuc.edu/wik
Accelerating Large-Scale Graph-based Nearest Neighbor Search on a Computational Storage Platform
K-nearest neighbor search is one of the fundamental tasks in various
applications and the hierarchical navigable small world (HNSW) has recently
drawn attention in large-scale cloud services, as it easily scales up the
database while offering fast search. On the other hand, a computational storage
device (CSD) that combines programmable logic and storage modules on a single
board becomes popular to address the data bandwidth bottleneck of modern
computing systems. In this paper, we propose a computational storage platform
that can accelerate a large-scale graph-based nearest neighbor search algorithm
based on SmartSSD CSD. To this end, we modify the algorithm more amenable on
the hardware and implement two types of accelerators using HLS- and RTL-based
methodology with various optimization methods. In addition, we scale up the
proposed platform to have 4 SmartSSDs and apply graph parallelism to boost the
system performance further. As a result, the proposed computational storage
platform achieves 75.59 query per second throughput for the SIFT1B dataset at
258.66W power dissipation, which is 12.83x and 17.91x faster and 10.43x and
24.33x more energy efficient than the conventional CPU-based and GPU-based
server platform, respectively. With multi-terabyte storage and custom
acceleration capability, we believe that the proposed computational storage
platform is a promising solution for cost-sensitive cloud datacenters.Comment: Extension of FCCM 20201 and Accepted in Transaction on Computer
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