6,401 research outputs found
Fine-grained visualization pipelines and lazy functional languages
The pipeline model in visualization has evolved from a conceptual model of data processing into a widely used architecture for implementing visualization systems. In the process, a number of capabilities have been introduced, including streaming of data in chunks, distributed pipelines, and demand-driven processing. Visualization systems have invariably built on stateful programming technologies, and these capabilities have had to be implemented explicitly within the lower layers of a complex hierarchy of services. The good news for developers is that applications built on top of this hierarchy can access these capabilities without concern for how they are implemented. The bad news is that by freezing capabilities into low-level services expressive power and flexibility is lost. In this paper we express visualization systems in a programming language that more naturally supports this kind of processing model. Lazy functional languages support fine-grained demand-driven processing, a natural form of streaming, and pipeline-like function composition for assembling applications. The technology thus appears well suited to visualization applications. Using surface extraction algorithms as illustrative examples, and the lazy functional language Haskell, we argue the benefits of clear and concise expression combined with fine-grained, demand-driven computation. Just as visualization provides insight into data, functional abstraction provides new insight into visualization
Gunrock: GPU Graph Analytics
For large-scale graph analytics on the GPU, the irregularity of data access
and control flow, and the complexity of programming GPUs, have presented two
significant challenges to developing a programmable high-performance graph
library. "Gunrock", our graph-processing system designed specifically for the
GPU, uses a high-level, bulk-synchronous, data-centric abstraction focused on
operations on a vertex or edge frontier. Gunrock achieves a balance between
performance and expressiveness by coupling high performance GPU computing
primitives and optimization strategies with a high-level programming model that
allows programmers to quickly develop new graph primitives with small code size
and minimal GPU programming knowledge. We characterize the performance of
various optimization strategies and evaluate Gunrock's overall performance on
different GPU architectures on a wide range of graph primitives that span from
traversal-based algorithms and ranking algorithms, to triangle counting and
bipartite-graph-based algorithms. The results show that on a single GPU,
Gunrock has on average at least an order of magnitude speedup over Boost and
PowerGraph, comparable performance to the fastest GPU hardwired primitives and
CPU shared-memory graph libraries such as Ligra and Galois, and better
performance than any other GPU high-level graph library.Comment: 52 pages, invited paper to ACM Transactions on Parallel Computing
(TOPC), an extended version of PPoPP'16 paper "Gunrock: A High-Performance
Graph Processing Library on the GPU
On Designing Multicore-aware Simulators for Biological Systems
The stochastic simulation of biological systems is an increasingly popular
technique in bioinformatics. It often is an enlightening technique, which may
however result in being computational expensive. We discuss the main
opportunities to speed it up on multi-core platforms, which pose new challenges
for parallelisation techniques. These opportunities are developed in two
general families of solutions involving both the single simulation and a bulk
of independent simulations (either replicas of derived from parameter sweep).
Proposed solutions are tested on the parallelisation of the CWC simulator
(Calculus of Wrapped Compartments) that is carried out according to proposed
solutions by way of the FastFlow programming framework making possible fast
development and efficient execution on multi-cores.Comment: 19 pages + cover pag
Profiling user activities with minimal traffic traces
Understanding user behavior is essential to personalize and enrich a user's
online experience. While there are significant benefits to be accrued from the
pursuit of personalized services based on a fine-grained behavioral analysis,
care must be taken to address user privacy concerns. In this paper, we consider
the use of web traces with truncated URLs - each URL is trimmed to only contain
the web domain - for this purpose. While such truncation removes the
fine-grained sensitive information, it also strips the data of many features
that are crucial to the profiling of user activity. We show how to overcome the
severe handicap of lack of crucial features for the purpose of filtering out
the URLs representing a user activity from the noisy network traffic trace
(including advertisement, spam, analytics, webscripts) with high accuracy. This
activity profiling with truncated URLs enables the network operators to provide
personalized services while mitigating privacy concerns by storing and sharing
only truncated traffic traces.
In order to offset the accuracy loss due to truncation, our statistical
methodology leverages specialized features extracted from a group of
consecutive URLs that represent a micro user action like web click, chat reply,
etc., which we call bursts. These bursts, in turn, are detected by a novel
algorithm which is based on our observed characteristics of the inter-arrival
time of HTTP records. We present an extensive experimental evaluation on a real
dataset of mobile web traces, consisting of more than 130 million records,
representing the browsing activities of 10,000 users over a period of 30 days.
Our results show that the proposed methodology achieves around 90% accuracy in
segregating URLs representing user activities from non-representative URLs
Unsupervised Human Action Detection by Action Matching
We propose a new task of unsupervised action detection by action matching.
Given two long videos, the objective is to temporally detect all pairs of
matching video segments. A pair of video segments are matched if they share the
same human action. The task is category independent---it does not matter what
action is being performed---and no supervision is used to discover such video
segments. Unsupervised action detection by action matching allows us to align
videos in a meaningful manner. As such, it can be used to discover new action
categories or as an action proposal technique within, say, an action detection
pipeline. Moreover, it is a useful pre-processing step for generating video
highlights, e.g., from sports videos.
We present an effective and efficient method for unsupervised action
detection. We use an unsupervised temporal encoding method and exploit the
temporal consistency in human actions to obtain candidate action segments. We
evaluate our method on this challenging task using three activity recognition
benchmarks, namely, the MPII Cooking activities dataset, the THUMOS15 action
detection benchmark and a new dataset called the IKEA dataset. On the MPII
Cooking dataset we detect action segments with a precision of 21.6% and recall
of 11.7% over 946 long video pairs and over 5000 ground truth action segments.
Similarly, on THUMOS dataset we obtain 18.4% precision and 25.1% recall over
5094 ground truth action segment pairs.Comment: IEEE International Conference on Computer Vision and Pattern
Recognition CVPR 2017 Workshop
Toward Open and Programmable Wireless Network Edge
Increasingly, the last hop connecting users to their enterprise and home networks is wireless. Wireless is becoming ubiquitous not only in homes and enterprises but in public venues such as coffee shops, hospitals, and airports. However, most of the publicly and privately available wireless networks are proprietary and closed in operation. Also, there is little effort from industries to move forward on a path to greater openness for the requirement of innovation. Therefore, we believe it is the domain of university researchers to enable innovation through openness. In this thesis work, we introduce and defines the importance of open framework in addressing the complexity of the wireless network. The Software Defined Network (SDN) framework has emerged as a popular solution for the data center network. However, the promise of the SDN framework is to make the network open, flexible and programmable. In order to deliver on the promise, SDN must work for all users and across all networks, both wired and wireless. Therefore, we proposed to create new modules and APIs to extend the standard SDN framework all the way to the end-devices (i.e., mobile devices, APs). Thus, we want to provide an extensible and programmable abstraction of the wireless network as part of the current SDN-based solution. In this thesis work, we design and develop a framework, weSDN (wireless extension of SDN), that extends the SDN control capability all the way to the end devices to support client-network interaction capabilities and new services. weSDN enables the control-plane of wireless networks to be extended to mobile devices and allows for top-level decisions to be made from an SDN controller with knowledge of the network as a whole, rather than device centric configurations. In addition, weSDN easily obtains user application information, as well as the ability to monitor and control application flows dynamically. Based on the weSDN framework, we demonstrate new services such as application-aware traffic management, WLAN virtualization, and security management
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