Software transactional memories for Scala

Goodman, Daniel; Khan, Behram; Khan, Salman; Luján, Mikel; Watson, Ian

doi:10.1016/j.jpdc.2012.09.015

Cited by 9 publications

(4 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…SpiNNaker is built using a million ARM 968 processor cores (of which 60% are currently available). Each chip on the machine includes 18 processor cores connected by a network on chip (NoC) communication system (Grymel and Furber, 2011 ; Furber, 2012 ; Furber et al, 2013 , 2014 ; Goodman et al, 2013 ; Painkras et al, 2013 ). This fundamentally different architecture paradigm, besides bespoke design for neurobiology simulations, makes the SpiNNaker system interesting for exploring new implementations of stochastic searches.…”

Section: Methodsmentioning

confidence: 99%

Using Stochastic Spiking Neural Networks on SpiNNaker to Solve Constraint Satisfaction Problems

Guerra

Furber

2017

Front. Neurosci.

View full text Add to dashboard Cite

Constraint satisfaction problems (CSP) are at the core of numerous scientific and technological applications. However, CSPs belong to the NP-complete complexity class, for which the existence (or not) of efficient algorithms remains a major unsolved question in computational complexity theory. In the face of this fundamental difficulty heuristics and approximation methods are used to approach instances of NP (e.g., decision and hard optimization problems). The human brain efficiently handles CSPs both in perception and behavior using spiking neural networks (SNNs), and recent studies have demonstrated that the noise embedded within an SNN can be used as a computational resource to solve CSPs. Here, we provide a software framework for the implementation of such noisy neural solvers on the SpiNNaker massively parallel neuromorphic hardware, further demonstrating their potential to implement a stochastic search that solves instances of P and NP problems expressed as CSPs. This facilitates the exploration of new optimization strategies and the understanding of the computational abilities of SNNs. We demonstrate the basic principles of the framework by solving difficult instances of the Sudoku puzzle and of the map color problem, and explore its application to spin glasses. The solver works as a stochastic dynamical system, which is attracted by the configuration that solves the CSP. The noise allows an optimal exploration of the space of configurations, looking for the satisfiability of all the constraints; if applied discontinuously, it can also force the system to leap to a new random configuration effectively causing a restart.

show abstract

Section: Methodsmentioning

confidence: 99%

Using Stochastic Spiking Neural Networks on SpiNNaker to Solve Constraint Satisfaction Problems

Guerra

Furber

2017

Front. Neurosci.

View full text Add to dashboard Cite

show abstract

“…To generate a qualitative assessment of the difference that the scheduling order can make to a dataflow execution and to experiment with how such priority information may be cleanly added to the dataflow programs we used a modified version of DFScala [7] with the MUTS [8] software transactional memory to add mutable state. We then constructed several benchmarks with support for prioritised tasks and monitored the following parameters:…”

Section: Methodsmentioning

confidence: 99%

Improved Dataflow Executions with User Assisted Scheduling

Goodman

Khan

Luján

et al. 2013

2013 Data-Flow Execution Models for Extreme Scale Computing

Self Cite

View full text Add to dashboard Cite

Abstract-In pure dataflow applications scheduling can have a huge effect on the memory footprint and number of active tasks in the program. However, in impure programs, scheduling not only effects the system resources, but can also effect the overall time complexity and accuracy of the program. To address both of these aspects this paper describes and analyses effective extensions to a dataflow scheduler to allow programmers to provide priority information describing the preferred execution order of a dataflow graph. We demonstrate that even very crude task priority metrics can be extremely effective, providing an average saving of 91% over the worst case scenario and 60% over the best case naive scenario. We also note that by specifying the scheduling information explicitly based on the algorithm, not the hardware, we provide portability to the application.

show abstract

“…The recent versions of Scala integrate the Akka Framework [11] for implementing actors. Scala also supports transactional memory (TM) [12], a programming model that provides atomicity, isolation, and rollback capabilities within transactional code regions [13]. TM provides built-in support for checkpointing and rollback, which we exploit for controlling concurrent message processing.…”

Section: Background and Related Workmentioning

confidence: 99%

Dynamic Message Processing and Transactional Memory in the Actor Model

Hayduk

Sobe

Felber

2015

Distributed Applications and Interoperable Systems

View full text Add to dashboard Cite

Abstract. With the trend of ever growing data centers and scaling core counts, simple programming models for efficient distributed and concurrent programming are required. One of the successful principles for scalable computing is the actor model, which is based on message passing. Actors are objects that hold local state that can only be modified by the exchange of messages. To avoid typical concurrency hazards, each actor processes messages sequentially. However, this limits the scalability of the model. We have shown in former work that concurrent message processing can be implemented with the help of transactional memory, ensuring sequential processing, when required. This approach is advantageous in low contention phases, however, does not scale for high contention phases. In this paper we introduce a combination of dynamic resource allocation and non-transactional message processing to overcome this limitation. This allows for efficient resource utilization as these two mechanisms can be handled in parallel. We show that we can substantially reduce the execution time of high-contention workloads in a micro-benchmark as well as in a real-world application.

show abstract

Software transactional memories for Scala

Cited by 9 publications

References 1 publication

Using Stochastic Spiking Neural Networks on SpiNNaker to Solve Constraint Satisfaction Problems

Using Stochastic Spiking Neural Networks on SpiNNaker to Solve Constraint Satisfaction Problems

Improved Dataflow Executions with User Assisted Scheduling

Dynamic Message Processing and Transactional Memory in the Actor Model

Contact Info

Product

Resources

About