2,202 research outputs found
Tupleware: Redefining Modern Analytics
There is a fundamental discrepancy between the targeted and actual users of
current analytics frameworks. Most systems are designed for the data and
infrastructure of the Googles and Facebooks of the world---petabytes of data
distributed across large cloud deployments consisting of thousands of cheap
commodity machines. Yet, the vast majority of users operate clusters ranging
from a few to a few dozen nodes, analyze relatively small datasets of up to a
few terabytes, and perform primarily compute-intensive operations. Targeting
these users fundamentally changes the way we should build analytics systems.
This paper describes the design of Tupleware, a new system specifically aimed
at the challenges faced by the typical user. Tupleware's architecture brings
together ideas from the database, compiler, and programming languages
communities to create a powerful end-to-end solution for data analysis. We
propose novel techniques that consider the data, computations, and hardware
together to achieve maximum performance on a case-by-case basis. Our
experimental evaluation quantifies the impact of our novel techniques and shows
orders of magnitude performance improvement over alternative systems
Synthesizing Functional Reactive Programs
Functional Reactive Programming (FRP) is a paradigm that has simplified the
construction of reactive programs. There are many libraries that implement
incarnations of FRP, using abstractions such as Applicative, Monads, and
Arrows. However, finding a good control flow, that correctly manages state and
switches behaviors at the right times, still poses a major challenge to
developers. An attractive alternative is specifying the behavior instead of
programming it, as made possible by the recently developed logic: Temporal
Stream Logic (TSL). However, it has not been explored so far how Control Flow
Models (CFMs), as synthesized from TSL specifications, can be turned into
executable code that is compatible with libraries building on FRP. We bridge
this gap, by showing that CFMs are indeed a suitable formalism to be turned
into Applicative, Monadic, and Arrowized FRP. We demonstrate the effectiveness
of our translations on a real-world kitchen timer application, which we
translate to a desktop application using the Arrowized FRP library Yampa, a web
application using the Monadic threepenny-gui library, and to hardware using the
Applicative hardware description language ClaSH.Comment: arXiv admin note: text overlap with arXiv:1712.0024
Optimizing Associative Information Transfer within Content-addressable Memory
Original article can be found at: http://www.oldcitypublishing.com/IJUC/IJUC.htmlPeer reviewe
A new class of neural architectures to model episodic memory : computational studies of distal reward learning
A computational cognitive neuroscience model is proposed, which models episodic memory based on the mammalian brain. A computational neural architecture instantiates the proposed model and is tested on a particular task of distal reward learning. Categorical Neural Semantic Theory informs the architecture design. To experiment upon the computational brain model, embodiment and an environment in which the embodiment exists are simulated. This simulated environment realizes the Morris Water Maze task, a well established biological experimental test of distal reward learning. The embodied neural architecture is treated as a virtual rat and the environment it acts in as a virtual water tank. Performance levels of the neural architectures are evaluated through analysis of embodied behavior in the distal reward learning task. Comparison is made to biological rat experimental data, as well as comparison to other published models. In addition, differences in performance are compared between the normal and categorically informed versions of the architecture
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