18,879 research outputs found
Regulatory Institutions in Agricultural Markets: A Comparative Analysis
We have employed a simple model to analyse market regulation in a situation with multifunctional agricultural production, i.e., a public good produced jointly with a private good, and where there is imperfect competition in processing. We have analysed the impact on welfare of two archetype regulatory institutions formed to overcome the market imperfections. The institutions, a Regulatory Marketing Board and a Regulatory Marketing Cooperative, are both represented in the Norwegian agricultural market. Taking into account the cost of public funds, we find that the Board in general ensures the highest social welfare. The Cooperative does not replicate the Board solution unless restricted by a price cap and in combination with a production subsidy. If the restricted Cooperative is able to practise a higher degree of cost sharing than the Board, it may however produce the highest welfare.cooperative, marketing board, multifunctionality, oligopsony, trade, Political Economy,
Distributed data cache designs for clustered VLIW processors
Wire delays are a major concern for current and forthcoming processors. One approach to deal with this problem is to divide the processor into semi-independent units referred to as clusters. A cluster usually consists of a local register file and a subset of the functional units, while the L1 data cache typically remains centralized in What we call partially distributed architectures. However, as technology evolves, the relative latency of such a centralized cache will increase, leading to an important impact on performance. In this paper, we propose partitioning the L1 data cache among clusters for clustered VLIW processors. We refer to this kind of design as fully distributed processors. In particular; we propose and evaluate three different configurations: a snoop-based cache coherence scheme, a word-interleaved cache, and flexible LO-buffers managed by the compiler. For each alternative, instruction scheduling techniques targeted to cyclic code are developed. Results for the Mediabench suite'show that the performance of such fully distributed architectures is always better than the performance of a partially distributed one with the same amount of resources. In addition, the key aspects of each fully distributed configuration are explored.Peer ReviewedPostprint (published version
On statistics, computation and scalability
How should statistical procedures be designed so as to be scalable
computationally to the massive datasets that are increasingly the norm? When
coupled with the requirement that an answer to an inferential question be
delivered within a certain time budget, this question has significant
repercussions for the field of statistics. With the goal of identifying
"time-data tradeoffs," we investigate some of the statistical consequences of
computational perspectives on scability, in particular divide-and-conquer
methodology and hierarchies of convex relaxations.Comment: Published in at http://dx.doi.org/10.3150/12-BEJSP17 the Bernoulli
(http://isi.cbs.nl/bernoulli/) by the International Statistical
Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm
Dynamic load balancing for the distributed mining of molecular structures
In molecular biology, it is often desirable to find common properties in large numbers of drug candidates. One family of
methods stems from the data mining community, where algorithms to find frequent graphs have received increasing attention over the
past years. However, the computational complexity of the underlying problem and the large amount of data to be explored essentially
render sequential algorithms useless. In this paper, we present a distributed approach to the frequent subgraph mining problem to
discover interesting patterns in molecular compounds. This problem is characterized by a highly irregular search tree, whereby no
reliable workload prediction is available. We describe the three main aspects of the proposed distributed algorithm, namely, a dynamic
partitioning of the search space, a distribution process based on a peer-to-peer communication framework, and a novel receiverinitiated
load balancing algorithm. The effectiveness of the distributed method has been evaluated on the well-known National Cancer
Institute’s HIV-screening data set, where we were able to show close-to linear speedup in a network of workstations. The proposed
approach also allows for dynamic resource aggregation in a non dedicated computational environment. These features make it suitable
for large-scale, multi-domain, heterogeneous environments, such as computational grids
Asynchronous spiking neurons, the natural key to exploit temporal sparsity
Inference of Deep Neural Networks for stream signal (Video/Audio) processing in edge devices is still challenging. Unlike the most state of the art inference engines which are efficient for static signals, our brain is optimized for real-time dynamic signal processing. We believe one important feature of the brain (asynchronous state-full processing) is the key to its excellence in this domain. In this work, we show how asynchronous processing with state-full neurons allows exploitation of the existing sparsity in natural signals. This paper explains three different types of sparsity and proposes an inference algorithm which exploits all types of sparsities in the execution of already trained networks. Our experiments in three different applications (Handwritten digit recognition, Autonomous Steering and Hand-Gesture recognition) show that this model of inference reduces the number of required operations for sparse input data by a factor of one to two orders of magnitudes. Additionally, due to fully asynchronous processing this type of inference can be run on fully distributed and scalable neuromorphic hardware platforms
A Framework to Analyze the Performance of Load Balancing Schemes for Ensembles of Stochastic Simulations
Ensembles of simulations are employed to estimate the statistics of possible future states of a system, and are widely used in important applications such as climate change and biological modeling. Ensembles of runs can naturally be executed in parallel. However, when the CPU times of individual simulations vary considerably, a simple strategy of assigning an equal number of tasks per processor can lead to serious work imbalances and low parallel efficiency. This paper presents a new probabilistic framework to analyze the performance of dynamic load balancing algorithms for ensembles of simulations where many tasks are mapped onto each processor, and where the individual compute times vary considerably among tasks. Four load balancing strategies are discussed: most-dividing, all-redistribution, random-polling, and neighbor-redistribution. Simulation results with a stochastic budding yeast cell cycle model is consistent with the theoretical analysis. It is especially significant that there is a provable global decrease in load imbalance for the local rebalancing algorithms due to scalability concerns for the global rebalancing algorithms. The overall simulation time is reduced by up to 25%, and the total processor idle time by 85%
Solving the Klein-Gordon equation using Fourier spectral methods: A benchmark test for computer performance
The cubic Klein-Gordon equation is a simple but non-trivial partial
differential equation whose numerical solution has the main building blocks
required for the solution of many other partial differential equations. In this
study, the library 2DECOMP&FFT is used in a Fourier spectral scheme to solve
the Klein-Gordon equation and strong scaling of the code is examined on
thirteen different machines for a problem size of 512^3. The results are useful
in assessing likely performance of other parallel fast Fourier transform based
programs for solving partial differential equations. The problem is chosen to
be large enough to solve on a workstation, yet also of interest to solve
quickly on a supercomputer, in particular for parametric studies. Unlike other
high performance computing benchmarks, for this problem size, the time to
solution will not be improved by simply building a bigger supercomputer.Comment: 10 page
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