General-Purpose Stream Processing

This article presents a real-time Fuzzy ART neural classifier for skin segmentation implemented on a Graphics Processing Unit (GPU). GPUs have evolved into powerful programmable processors, becoming increasingly used in time-dependent research fields such as dynamics simulation, database management, computer vision or image processing. GPUs are designed following a Stream Processing Model and each new generation of commodity graphics cards incorporates rather more powerful and flexible GPUs (Owens, 2005). In the last years General Purpose GPU (GPGPU) computing has established as a well-accepted application acceleration technique. The GPGPU phenomenon belongs to larger research areas: homogeneous and heterogenous multi-core computing. Research in these fields is driven by factors as the Moore’s Gap. Today’s uni-processors follow a 90/100 rule, where 90 percent of the processor is passive and 10 percent is doing active work. By contrast, multi-core processors try to follow the same general rule but with 10 percent passive and 90 percent active processors when working at full throughput. Single processor Central Processing Units (CPUs) were designed for executing general purpose programs comprised of sequential instructions operating on single data. Designers tried to optimize complex control requirements with minimum latency, thus many transistors in the chip are devoted to branch prediction, out of order execution and caching. In the article Stream Processing of a Neural Classifier I several terms and concepts related to GPGPU were introduced. A detailed description of the Fuzzy ART ANN implementation on a commodity graphics card, exploiting the GPU’s parallelism and vector capabilities, was given. In this article, the aforementioned Fuzzy ART GPU-designed implementation is configured for robust real-time skin recognition. Both learning and testing processes are done on the GPU using chrominance components in TSL (Tint, Saturation and Luminance) color space. The Fuzzy ART ANN implementation recognizes skin tone pixels at a rate of 270 fps on an NVIDIA GF7800GTX GPU.

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SPOC: GPGPU PROGRAMMING THROUGH STREAM PROCESSING WITH OCAML

Parallel Processing Letters ◽

10.1142/s0129626412400075 ◽

2012 ◽

Vol 22 (02) ◽

pp. 1240007 ◽

Cited By ~ 8

Author(s):

MATHIAS BOURGOIN ◽

EMMANUEL CHAILLOUX ◽

JEAN-LUC LAMOTTE

Keyword(s):

Graphics Processing Units ◽

High Performance ◽

Stream Processing ◽

General Purpose ◽

Data Sets ◽

Specific Data ◽

Garbage Collector ◽

Great Performance ◽

Graphics Processing

General purpose computing on graphics processing units (GPGPU) consists of using GPUs to handle computations commonly handled by CPUs. GPGPU programming implies developing specific programs to run on GPUs managed by a host program running on the CPU. To achieve high performance implies to explicitly organize memory transfers between devices. Besides, different incompatible frameworks exist making productivity and portability difficult to achieve. In this paper, we describe SPOC, an OCaml library, defining specific data sets in order to automatically manage transfers between GPU and CPU. SPOC also offers a runtime library looking for multiple frameworks and making them usable transparently. We also describe the link between SPOC and the OCaml garbage collector to optimize transfers dynamically. SPOC benchmarks show that SPOC can offer great performance while simplifying GPGPU programming

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Algebraic Stream Processing

10.23889/suthesis.57505 ◽

1995 ◽

Author(s):

◽

S. Stephens

Keyword(s):

Broad Class ◽

Stream Processing ◽

General Purpose ◽

Second Order ◽

First Order ◽

Recursive Functions ◽

Sound Theory ◽

Alternative Approach ◽

Develop Software ◽

Primitive Recursive

We identify and analyse the typically higher-order approaches to stream processing in the literature. From this analysis we motivate an alternative approach to the specification of SPSs as STs based on an essentially first-order equational representation. This technique is called Cartesian form specification. More specifically, while STs are properly second-order objects we show that using Cartesian forms, the second-order models needed to formalise STs are so weak that we may use and develop well-understood first-order methods from computability theory and mathematical logic to reason about their properties. Indeed, we show that by specifying STs equationally in Cartesian form as primitive recursive functions we have the basis of a new, general purpose and mathematically sound theory of stream processing that emphasises the formal specification and formal verification of STs. The main topics that we address in the development of this theory are as follows. We present a theoretically well-founded general purpose stream processing language ASTRAL (Algebraic Stream TRAnsformer Language) that supports the use of modular specification techniques for full second-order STs. We show how ASTRAL specifications can be given a Cartesian form semantics using the language PREQ that is an equational characterisation of the primitive recursive functions. In more detail, we show that by compiling ASTRAL specifications into an equivalent Cartesian form in PREQ we can use first-order equational logic with induction as a logical calculus to reason about STs. In particular, using this calculus we identify a syntactic class of correctness statements for which the verification of ASTRAL programmes is decidable relative to this calculus. We define an effective algorithm based on term re-writing techniques to implement this calculus and hence to automatically verify a very broad class of STs including conventional hardware devices. Finally, we analyse the properties of this abstract algorithm as a proof assistant and discuss various techniques that have been adopted to develop software tools based on this algorithm.

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Data Stream Processing for Packet-Level Analytics

Sensors ◽

10.3390/s21051735 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1735

Author(s):

Alessandra Fais ◽

Giuseppe Lettieri ◽

Gregorio Procissi ◽

Stefano Giordano ◽

Francesco Oppedisano

Keyword(s):

High Performance ◽

Stream Processing ◽

Poor Performance ◽

Expressive Power ◽

General Purpose ◽

Critical Event ◽

Unified Framework ◽

Data Rates ◽

General Complex ◽

Definition Of

One of the most challenging tasks for network operators is implementing accurate per-packet monitoring, looking for signs of performance degradation, security threats, and so on. Upon critical event detection, corrective actions must be taken to keep the network running smoothly. Implementing this mechanism requires the analysis of packet streams in a real-time (or close to) fashion. In a softwarized network context, Stream Processing Systems (SPSs) can be adopted for this purpose. Recent solutions based on traditional SPSs, such as Storm and Flink, can support the definition of general complex queries, but they show poor performance at scale. To handle input data rates in the order of gigabits per seconds, programmable switch platforms are typically used, although they offer limited expressiveness. With the proposed approach, we intend to offer high performance and expressive power in a unified framework by solely relying on SPSs for multicores. Captured packets are translated into a proper tuple format, and network monitoring queries are applied to tuple streams. Packet analysis tasks are expressed as streaming pipelines, running on general-purpose programmable network devices, and a second stage of elaboration can process aggregated statistics from different devices. Experiments carried out with an example monitoring application show that the system is able to handle realistic traffic at a 10 Gb/s speed. The same application scales almost up to 20 Gb/s speed thanks to the simple optimizations of the underlying framework. Hence, the approach proves to be viable and calls for the investigation of more extensive optimizations to support more complex elaborations and higher data rates.

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The economics of selection of mail orders Drs. Zahavi and Levin are the masterminds behind the development of AMOS, a customized predictive modeling system for the Franklin Mint in Philadelphia, and GainSmarts, a general purpose data mining system that is the two-time winner of the KDD-CUP competition for the best data mining tools (1997 and 1998) sponsored by the American Association for Artificial Intelligence.

Journal of Interactive Marketing ◽

10.1002/dir.1016.abs ◽

2001 ◽

Vol 15 (3) ◽

pp. 53

Author(s):

Nissan Levin ◽

Jacob Zahavi

Keyword(s):

Artificial Intelligence ◽

Data Mining ◽

Predictive Modeling ◽

American Association ◽

General Purpose ◽

Mining System ◽

Data Mining System ◽

Mining Tools ◽

Selection Of

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Use of an interactive general-purpose computer terminal to simulate training equipment operation.

PsycEXTRA Dataset ◽

10.1037/e441802004-001 ◽

1975 ◽

Cited By ~ 1

Author(s):

George F. Lahey ◽

Alice M. Crawford ◽

Richard E. Hurlock

Keyword(s):

General Purpose ◽

Computer Terminal ◽

Equipment Operation ◽

General Purpose Computer ◽

Purpose Computer

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The general purpose design of 2-dimensional recursive digital filters

IEE Proceedings G (Electronic Circuits and Systems) ◽

10.1049/ip-g-1.1983.0032 ◽

1983 ◽

Vol 130 (5) ◽

pp. 171 ◽

Cited By ~ 1

Author(s):

G. Crebbin ◽

J. Attikiouzel

Keyword(s):

Digital Filters ◽

General Purpose ◽

Recursive Digital Filters

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PENGARUH LATIHAN RANGE OF MOTION (ROM) AKTIF ASSITIF TERHADAP RENTANG GERAK SENDI PADA LANSIA YANG MENGALAMI IMMOBILISASI FISIK

Surya Medika: Jurnal Ilmiah Ilmu Keperawatan dan Ilmu Kesehatan Masyarakat ◽

10.32504/sm.v13i2.116 ◽

2019 ◽

Vol 13 (2) ◽

Author(s):

Andri Setyorini ◽

Niken Setyaningrum

Keyword(s):

Range Of Motion ◽

Human Life ◽

The Elderly ◽

General Purpose ◽

Test Statistic ◽

Physical Mobility ◽

Motion Exercise ◽

The One ◽

The Individual ◽

Active Training

Background: Elderly is the final stage of the human life cycle, that is part of the inevitable life process and will be experienced by every individual. At this stage the individual undergoes many changes both physically and mentally, especially setbacks in various functions and abilities he once had. Preliminary study in Social House Tresna Wreda Yogyakarta Budhi Luhur Units there are 16 elderly who experience physical immobilization. In the social house has done various activities for the elderly are still active, but the elderly who experienced muscle weakness is not able to follow the exercise, so it needs to do ROM (Range Of Motion) exercise. Objective: The general purpose of this research is to know the effect of Range Of Motion (ROM) Active Assitif training to increase the range of motion of joints in elderly who experience physical immobility at Social House of Tresna Werdha Yogyakarta unit Budhi Luhur. Methode: This study was included in the type of pre-experiment, using the One Group Pretest Posttest design in which the range of motion of the joints before (pretest) and posttest (ROM) was performed ROM. Subjects in this study were all elderly with impaired physical mobility in Social House Tresna Wreda Yogyakarta Unit Budhi Luhur a number of 14 elderly people. Data analysis in this research use paired sample t-test statistic Result: The result of this research shows that there is influence of ROM (Range of Motion) Active training to increase of range of motion of joints in elderly who experience physical immobility at Social House Tresna Wredha Yogyakarta Unit Budhi Luhur. Conclusion: There is influence of ROM (Range of Motion) Active training to increase of range of motion of joints in elderly who experience physical immobility at Social House Tresna Wredha Yogyakarta Unit Budhi Luhur.

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