5,502 research outputs found
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
Scaling of a large-scale simulation of synchronous slow-wave and asynchronous awake-like activity of a cortical model with long-range interconnections
Cortical synapse organization supports a range of dynamic states on multiple
spatial and temporal scales, from synchronous slow wave activity (SWA),
characteristic of deep sleep or anesthesia, to fluctuating, asynchronous
activity during wakefulness (AW). Such dynamic diversity poses a challenge for
producing efficient large-scale simulations that embody realistic metaphors of
short- and long-range synaptic connectivity. In fact, during SWA and AW
different spatial extents of the cortical tissue are active in a given timespan
and at different firing rates, which implies a wide variety of loads of local
computation and communication. A balanced evaluation of simulation performance
and robustness should therefore include tests of a variety of cortical dynamic
states. Here, we demonstrate performance scaling of our proprietary Distributed
and Plastic Spiking Neural Networks (DPSNN) simulation engine in both SWA and
AW for bidimensional grids of neural populations, which reflects the modular
organization of the cortex. We explored networks up to 192x192 modules, each
composed of 1250 integrate-and-fire neurons with spike-frequency adaptation,
and exponentially decaying inter-modular synaptic connectivity with varying
spatial decay constant. For the largest networks the total number of synapses
was over 70 billion. The execution platform included up to 64 dual-socket
nodes, each socket mounting 8 Intel Xeon Haswell processor cores @ 2.40GHz
clock rates. Network initialization time, memory usage, and execution time
showed good scaling performances from 1 to 1024 processes, implemented using
the standard Message Passing Interface (MPI) protocol. We achieved simulation
speeds of between 2.3x10^9 and 4.1x10^9 synaptic events per second for both
cortical states in the explored range of inter-modular interconnections.Comment: 22 pages, 9 figures, 4 table
Scaling of a large-scale simulation of synchronous slow-wave and asynchronous awake-like activity of a cortical model with long-range interconnections
Cortical synapse organization supports a range of dynamic states on multiple
spatial and temporal scales, from synchronous slow wave activity (SWA),
characteristic of deep sleep or anesthesia, to fluctuating, asynchronous
activity during wakefulness (AW). Such dynamic diversity poses a challenge for
producing efficient large-scale simulations that embody realistic metaphors of
short- and long-range synaptic connectivity. In fact, during SWA and AW
different spatial extents of the cortical tissue are active in a given timespan
and at different firing rates, which implies a wide variety of loads of local
computation and communication. A balanced evaluation of simulation performance
and robustness should therefore include tests of a variety of cortical dynamic
states. Here, we demonstrate performance scaling of our proprietary Distributed
and Plastic Spiking Neural Networks (DPSNN) simulation engine in both SWA and
AW for bidimensional grids of neural populations, which reflects the modular
organization of the cortex. We explored networks up to 192x192 modules, each
composed of 1250 integrate-and-fire neurons with spike-frequency adaptation,
and exponentially decaying inter-modular synaptic connectivity with varying
spatial decay constant. For the largest networks the total number of synapses
was over 70 billion. The execution platform included up to 64 dual-socket
nodes, each socket mounting 8 Intel Xeon Haswell processor cores @ 2.40GHz
clock rates. Network initialization time, memory usage, and execution time
showed good scaling performances from 1 to 1024 processes, implemented using
the standard Message Passing Interface (MPI) protocol. We achieved simulation
speeds of between 2.3x10^9 and 4.1x10^9 synaptic events per second for both
cortical states in the explored range of inter-modular interconnections.Comment: 22 pages, 9 figures, 4 table
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A Software Checking Framework Using Distributed Model Checking and Checkpoint/Resume of Virtualized PrOcess Domains
Complexity and heterogeneity of the deployed software applications often result in a wide range of dynamic states at runtime. The corner cases of software failure during execution often slip through the traditional software checking. If the software checking infrastructure supports the transparent checkpoint and resume of the live application states, the checking system can preserve and replay the live states in which the software failures occur. We introduce a novel software checking framework that enables application states including program behaviors and execution contexts to be cloned and resumed on a computing cloud. It employs (1) EXPLODE's model checking engine for a lightweight and general purpose software checking (2) ZAP system for faster, low overhead and transparent checkpoint and resume mechanism through virtualized PODs (PrOcess Domains), which is a collection of host-independent processes, and (3) scalable and distributed checking infrastructure based on Distributed EXPLODE. Efficient and portable checkpoint/resume and replay mechanism employed in this framework enables scalable software checking in order to improve the reliability of software products. The evaluation we conducted showed its feasibility, efficiency and applicability
Big Data, Internet Privacy and the Vulnerabilities of the African Regulatory Landscape
Social media generates massive amount of big data from users, the penetration of these platforms in Africa creates meaningful insights around customer needs and behaviour from the data. This helps to create new businesses that rebalance the technology and wealth gap in the continent. With every gigabytes of data generated brings about exploitation of customer data. Data Privacy becomes a focal point of concern. The global approaches to privacy for the users of social media platforms is still evolving but two jurisdictions have set a standard. The European General Data Protection Regulation (GDPR) and the Californian Consumer Privacy Act (CCPA) have provisions that are built on sustaining consumer consent and enabling consumers to be forgotten or have their consented data deleted at their own request. More so the exponential growth of the internet in Africa highlights the explosion of big data and there is a need to study its regulatory approaches in relation to the global best practices symbolized by the GDPR and the CCPA. The Paper reviews the African regulatory landscape and its approach to Big Data and possible vulnerable angles that exposes data of Africans on these social media platforms. It is clear that in spite of a continental treaty and a reasonable number of African countries with Data Privacy laws, these laws are in most of the countries either not built on strong legal grounds or lack an independent enforcement mechanism. Therefore the African approach leaves a lot of open issues and there is a need for a continental consensus on the best approach that will push through national legislations crept on a unifying continental model. Keywords: Big Data, Privacy, Regulatory, Social Media, Data analytics, Guidelines, rights DOI: 10.7176/EJBM/12-17-14 Publication date:June 30th 202
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