14,752 research outputs found
Energy-efficient pipelined bloom filters for network intrusion detection
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GridFTP: Protocol Extensions to FTP for the Grid
GridFTP: Protocol Extensions to FTP for the Gri
Evaluation of GPU/CPU Co-Processing Models for JPEG 2000 Packetization
With the bottom-line goal of increasing the
throughput of a GPU-accelerated JPEG 2000 encoder, this paper
evaluates whether the post-compression rate control and
packetization routines should be carried out on the CPU or on
the GPU. Three co-processing models that differ in how the
workload is split among the CPU and GPU are introduced. Both
routines are discussed and algorithms for executing them in
parallel are presented. Experimental results for compressing a
detail-rich UHD sequence to 4 bits/sample indicate speed-ups of
200x for the rate control and 100x for the packetization
compared to the single-threaded implementation in the
commercial Kakadu library. These two routines executed on the
CPU take 4x as long as all remaining coding steps on the GPU
and therefore present a bottleneck. Even if the CPU bottleneck
could be avoided with multi-threading, it is still beneficial to
execute all coding steps on the GPU as this minimizes the
required device-to-host transfer and thereby speeds up the
critical path from 17.2 fps to 19.5 fps for 4 bits/sample and to
22.4 fps for 0.16 bits/sample
A low cost reconfigurable soft processor for multimedia applications: design synthesis and programming model
This paper presents an FPGA implementation of a low cost 8 bit reconfigurable processor core for media processing applications. The core is optimized to provide all basic arithmetic and logic functions required by the media processing and other domains, as well as to make it easily integrable into a 2D array. This paper presents an investigation of the feasibility of the core as a potential soft processing architecture for FPGA platforms. The core was synthesized on the entire Virtex FPGA family to evaluate its overall performance, scalability and portability. A special feature of the proposed architecture is its simple programming model which allows low level programming. Throughput results for popular benchmarks coded using the programming model and cycle accurate simulator are presented
Handling Massive N-Gram Datasets Efficiently
This paper deals with the two fundamental problems concerning the handling of
large n-gram language models: indexing, that is compressing the n-gram strings
and associated satellite data without compromising their retrieval speed; and
estimation, that is computing the probability distribution of the strings from
a large textual source. Regarding the problem of indexing, we describe
compressed, exact and lossless data structures that achieve, at the same time,
high space reductions and no time degradation with respect to state-of-the-art
solutions and related software packages. In particular, we present a compressed
trie data structure in which each word following a context of fixed length k,
i.e., its preceding k words, is encoded as an integer whose value is
proportional to the number of words that follow such context. Since the number
of words following a given context is typically very small in natural
languages, we lower the space of representation to compression levels that were
never achieved before. Despite the significant savings in space, our technique
introduces a negligible penalty at query time. Regarding the problem of
estimation, we present a novel algorithm for estimating modified Kneser-Ney
language models, that have emerged as the de-facto choice for language modeling
in both academia and industry, thanks to their relatively low perplexity
performance. Estimating such models from large textual sources poses the
challenge of devising algorithms that make a parsimonious use of the disk. The
state-of-the-art algorithm uses three sorting steps in external memory: we show
an improved construction that requires only one sorting step thanks to
exploiting the properties of the extracted n-gram strings. With an extensive
experimental analysis performed on billions of n-grams, we show an average
improvement of 4.5X on the total running time of the state-of-the-art approach.Comment: Published in ACM Transactions on Information Systems (TOIS), February
2019, Article No: 2
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