8,094 research outputs found
Are Web Applications Ready for Parallelism?
In recent years, web applications have become pervasive. Their backbone is JavaScript, the only programming language supported by all major web browsers. Most browsers run on desktop or mobile devices with parallel hardware. However, JavaScript is by design sequential, and current web applications make little use of hardware parallelism. Are web applications ready to exploit parallel hardware? \ \ To answer this question we take a two-step approach. First, we survey 174 web developers regarding the potential and challenges of using parallelism. Then, we study the performance and computation shape of a set of web applications that are representative for the emerging web. We identify performance bottlenecks and examine memory access patterns to determine possible data parallelism. \ \ Our findings indicate that emerging web applications do have latent data parallelism, and JavaScript developers\u27 programming style are not a significant impediment to exploiting this parallelism
Workload-Based Configuration of MEMS-Based Storage Devices for Mobile Systems
Because of its small form factor, high capacity, and expected low cost, MEMS-based storage is a suitable storage technology for mobile systems. However, flash memory may outperform MEMS-based storage in terms of performance, and energy-efficiency. The problem is that MEMS-based storage devices have a large number (i.e., thousands) of heads, and to deliver peak performance, all heads must be deployed simultaneously to access each single sector. Since these devices are mechanical and thus some housekeeping information is needed for each head, this results in a huge capacity loss and increases the energy consumption of MEMS-based storage with respect to flash.
We solve this problem by proposing new techniques to lay out data in MEMS-based storage devices. Data layouts represent optimizations in a design space spanned by three parameters: the number of active heads, sector parallelism, and sector size. We explore this design space and show that by exploiting knowledge of the expected workload, MEMS-based devices can employ all heads, thus delivering peak performance, while decreasing the energy consumption and compromising only a little on the capacity. Our exploration shows that MEMS-based storage is competitive with flash in most cases, and outperforms flash in a few cases
Dynamic Virtual Page-based Flash Translation Layer with Novel Hot Data Identification and Adaptive Parallelism Management
Solid-state disks (SSDs) tend to replace traditional motor-driven hard disks in high-end storage devices in past few decades. However, various inherent features, such as out-of-place update [resorting to garbage collection (GC)] and limited endurance (resorting to wear leveling), need to be reduced to a large extent before that day comes. Both the GC and wear leveling fundamentally depend on hot data identification (HDI). In this paper, we propose a hot data-aware flash translation layer architecture based on a dynamic virtual page (DVPFTL) so as to improve the performance and lifetime of NAND flash devices. First, we develop a generalized dual layer HDI (DL-HDI) framework, which is composed of a cold data pre-classifier and a hot data post-identifier. Those can efficiently follow the frequency and recency of information access. Then, we design an adaptive parallelism manager (APM) to assign the clustered data chunks to distinct resident blocks in the SSD so as to prolong its endurance. Finally, the experimental results from our realized SSD prototype indicate that the DVPFTL scheme has reliably improved the parallelizability and endurance of NAND flash devices with improved GC-costs, compared with related works.Peer reviewe
Fog-enabled Edge Learning for Cognitive Content-Centric Networking in 5G
By caching content at network edges close to the users, the content-centric
networking (CCN) has been considered to enforce efficient content retrieval and
distribution in the fifth generation (5G) networks. Due to the volume,
velocity, and variety of data generated by various 5G users, an urgent and
strategic issue is how to elevate the cognitive ability of the CCN to realize
context-awareness, timely response, and traffic offloading for 5G applications.
In this article, we envision that the fundamental work of designing a cognitive
CCN (C-CCN) for the upcoming 5G is exploiting the fog computing to
associatively learn and control the states of edge devices (such as phones,
vehicles, and base stations) and in-network resources (computing, networking,
and caching). Moreover, we propose a fog-enabled edge learning (FEL) framework
for C-CCN in 5G, which can aggregate the idle computing resources of the
neighbouring edge devices into virtual fogs to afford the heavy delay-sensitive
learning tasks. By leveraging artificial intelligence (AI) to jointly
processing sensed environmental data, dealing with the massive content
statistics, and enforcing the mobility control at network edges, the FEL makes
it possible for mobile users to cognitively share their data over the C-CCN in
5G. To validate the feasibility of proposed framework, we design two
FEL-advanced cognitive services for C-CCN in 5G: 1) personalized network
acceleration, 2) enhanced mobility management. Simultaneously, we present the
simulations to show the FEL's efficiency on serving for the mobile users'
delay-sensitive content retrieval and distribution in 5G.Comment: Submitted to IEEE Communications Magzine, under review, Feb. 09, 201
Spatial Interference Detection for Mobile Visible Light Communication
Taking advantage of the rolling shutter effect of CMOS cameras in smartphones
is a common practice to increase the transfered data rate with visible light
communication (VLC) without employing external equipment such as photodiodes.
VLC can then be used as replacement of other marker based techniques for object
identification for Augmented Reality and Ubiquitous computing applications.
However, the rolling shutter effect only allows to transmit data over a single
dimension, which considerably limits the available bandwidth. In this article
we propose a new method exploiting spacial interference detection to enable
parallel transmission and design a protocol that enables easy identification of
interferences between two signals. By introducing a second dimension, we are
not only able to significantly increase the available bandwidth, but also
identify and isolate light sources in close proximity
RT3D: Achieving Real-Time Execution of 3D Convolutional Neural Networks on Mobile Devices
Mobile devices are becoming an important carrier for deep learning tasks, as
they are being equipped with powerful, high-end mobile CPUs and GPUs. However,
it is still a challenging task to execute 3D Convolutional Neural Networks
(CNNs) targeting for real-time performance, besides high inference accuracy.
The reason is more complex model structure and higher model dimensionality
overwhelm the available computation/storage resources on mobile devices. A
natural way may be turning to deep learning weight pruning techniques. However,
the direct generalization of existing 2D CNN weight pruning methods to 3D CNNs
is not ideal for fully exploiting mobile parallelism while achieving high
inference accuracy.
This paper proposes RT3D, a model compression and mobile acceleration
framework for 3D CNNs, seamlessly integrating neural network weight pruning and
compiler code generation techniques. We propose and investigate two structured
sparsity schemes i.e., the vanilla structured sparsity and kernel group
structured (KGS) sparsity that are mobile acceleration friendly. The vanilla
sparsity removes whole kernel groups, while KGS sparsity is a more fine-grained
structured sparsity that enjoys higher flexibility while exploiting full
on-device parallelism. We propose a reweighted regularization pruning algorithm
to achieve the proposed sparsity schemes. The inference time speedup due to
sparsity is approaching the pruning rate of the whole model FLOPs (floating
point operations). RT3D demonstrates up to 29.1 speedup in end-to-end
inference time comparing with current mobile frameworks supporting 3D CNNs,
with moderate 1%-1.5% accuracy loss. The end-to-end inference time for 16 video
frames could be within 150 ms, when executing representative C3D and R(2+1)D
models on a cellphone. For the first time, real-time execution of 3D CNNs is
achieved on off-the-shelf mobiles.Comment: To appear in Proceedings of the 35th AAAI Conference on Artificial
Intelligence (AAAI-21
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