23,962 research outputs found
Self-organizing lists on the Xnet
The first parallel designs for implementing self-organizing lists on the Xnet interconnection network are presented. Self-organizing lists permute the order of list entries after an entry is accessed according to some update hueristic. The heuristic attempts to place frequently requested entries closer to the front of the list. This paper outlines Xnet systems for self-organizing lists under the move-to-front and transpose update heuristics. Our novel designs can be used to achieve high-speed lossless text compression
HARQ Buffer Management: An Information-Theoretic View
A key practical constraint on the design of Hybrid automatic repeat request
(HARQ) schemes is the size of the on-chip buffer that is available at the
receiver to store previously received packets. In fact, in modern wireless
standards such as LTE and LTE-A, the HARQ buffer size is one of the main
drivers of the modem area and power consumption. This has recently highlighted
the importance of HARQ buffer management, that is, of the use of buffer-aware
transmission schemes and of advanced compression policies for the storage of
received data. This work investigates HARQ buffer management by leveraging
information-theoretic achievability arguments based on random coding.
Specifically, standard HARQ schemes, namely Type-I, Chase Combining and
Incremental Redundancy, are first studied under the assumption of a
finite-capacity HARQ buffer by considering both coded modulation, via Gaussian
signaling, and Bit Interleaved Coded Modulation (BICM). The analysis sheds
light on the impact of different compression strategies, namely the
conventional compression log-likelihood ratios and the direct digitization of
baseband signals, on the throughput. Then, coding strategies based on layered
modulation and optimized coding blocklength are investigated, highlighting the
benefits of HARQ buffer-aware transmission schemes. The optimization of
baseband compression for multiple-antenna links is also studied, demonstrating
the optimality of a transform coding approach.Comment: submitted to IEEE International Symposium on Information Theory
(ISIT) 2015. 29 pages, 12 figures, submitted to journal publicatio
Adaptive multiresolution computations applied to detonations
A space-time adaptive method is presented for the reactive Euler equations
describing chemically reacting gas flow where a two species model is used for
the chemistry. The governing equations are discretized with a finite volume
method and dynamic space adaptivity is introduced using multiresolution
analysis. A time splitting method of Strang is applied to be able to consider
stiff problems while keeping the method explicit. For time adaptivity an
improved Runge--Kutta--Fehlberg scheme is used. Applications deal with
detonation problems in one and two space dimensions. A comparison of the
adaptive scheme with reference computations on a regular grid allow to assess
the accuracy and the computational efficiency, in terms of CPU time and memory
requirements.Comment: Zeitschrift f\"ur Physicalische Chemie, accepte
Decoding billions of integers per second through vectorization
In many important applications -- such as search engines and relational
database systems -- data is stored in the form of arrays of integers. Encoding
and, most importantly, decoding of these arrays consumes considerable CPU time.
Therefore, substantial effort has been made to reduce costs associated with
compression and decompression. In particular, researchers have exploited the
superscalar nature of modern processors and SIMD instructions. Nevertheless, we
introduce a novel vectorized scheme called SIMD-BP128 that improves over
previously proposed vectorized approaches. It is nearly twice as fast as the
previously fastest schemes on desktop processors (varint-G8IU and PFOR). At the
same time, SIMD-BP128 saves up to 2 bits per integer. For even better
compression, we propose another new vectorized scheme (SIMD-FastPFOR) that has
a compression ratio within 10% of a state-of-the-art scheme (Simple-8b) while
being two times faster during decoding.Comment: For software, see https://github.com/lemire/FastPFor, For data, see
http://boytsov.info/datasets/clueweb09gap
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