Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 30 for “"memory models"”.
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Constructing and evaluating weak memory models
A memory model for an instruction set architecture (ISA) specifies all the legal multithreaded-program behaviors, and consequently constrains processor implementations. Weak memory models are a consequence of the desire of architects to preserve the flexibility of implementing optimizations that …
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Deep Memory Models and Efficient Reinforcement Learning under Partial Observability
… challenge is actually well known -- the use of memory. Memory is the storage and recall of sensory information for use in decision making, similar to the function of memory in humans and many other organisms. Memory enables such organisms to build and update internal representations of the …
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Analyzing el nino southern oscillation predictions from long-short-merm-memory models
… world. Because of its prevalence, scientists run models to forecast its next move. Here, long-short-term-memory models (LSTM) were compared to linear regression models (LR) as first steps to explore the potential benefits of simple deep neural networks for predicting ENSO. Each model’s prediction …
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Comparing vector-based and ACT-R memory models using large-scale datasets: User-customized hashtag and tag prediction on Twitter and StackOverflow
… sites provides unique opportunities to study models of declarative memory. The tasks of choosing a hashtag for a tweet and tagging a post on StackOverflow were framed as declarative memory retrieval problems. Two state-of-the-art cognitively-plausible declarative memory models were evaluated …
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Memory consistency models for high performance distributed computing
… of high performance distributed shared memory multiprocessors. This framework is based on computations, which specify the operations requested and constraints on how these operations may be applied; we call the framework computation-centric. This framework is expressive enough to specify …
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Thread Escape Analysis for a Memory Consistency -Aware Compiler
… and C#, have focused attention on the issue of memory model design. The Pensieve Project is building a compiler that will enable both language designers to prototype different memory models; and optimizing compilers to adapt to different memory models. Among the key analyses required to …
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A parallel numerical solver using hierarchically tiled arrays
… Current implementations target both shared-memory and distributed-memory models. In this thesis we present a proof-of-concept for portable linear solvers. We implement two algorithms from the SPIKE family using the HTA library. We show that our implementations of SPIKE exploit the …
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A schematic perspective of memory
"An analytical study of the different memory models proposed in the literature suggests the existence of a fourth memory mechanism, i.e., an intermediate-term memory, located between the short- and long-term memory. To the best of the author's knowledge there has never been any attempt made to …
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Bridging the gap between learning and memory
This thesis uses a fusion of recognition memory and implicit learning methods to investigate performance based on implicit learning. A series of experiments exposed participants to a study list composed of natural words that conform to a conjunctive rule-set involving the frequency and the …
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Exploring the Relationships Between Children's Working Memory and Long-Term Memory
Working memory and long-term memory are two types of memory associated with children’s learning and academic performance. A number of memory models have suggested there is a relationship between working memory and long-term memory; however, there is a lack of empirical research measuring this …
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Prefetching Vs The Memory System : Optimizations for Multi-core Server Platforms
… scheme for servers with respect to realistic memory systems. A large body of research work has been done in prefetching, even for server workloads that have sparse locality. Real systems disable prefetching in server settings, suggesting that there is a fundamental disconnect between research …
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The Energy Landscape of Associative Memory Energy Functions and Their Application to Ab Initio Protein Structure Prediction
… In particular the class of associative memory models are explicitly based on energy landscape ideas. In an associative memory model the energy of a particular conformation is a function of a set of sequence-structure correlations learned from a database of known structures. By varying …
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Specifying and verifying program transformations with PTRANS
… especially in the presence of relaxed memory models. In this thesis, we outline a Verification Framework for Optimizations and Program Transformations, designed to facilitate stating and reasoning about compiler optimizations and transformations on parallel programs. Most verified …
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Local versus global tables in Minimax Game Search
… The tables can be implemented using different memory models: global tables, worker-local tables and processor-local tables. Depending on whether each heuristic table depends on locality in the game tree, a memory model might be more suitable than others. This thesis describes an experiment that …
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Optimizing work stealing algorithms with scheduling constraints
… schedules and space bounds on shared-memory architectures with uniform memory models. However, the advent of hierarchical, non-uniform multicore systems and large-scale distributed-memory architectures has reduced the efficacy of these scheduling policies. Furthermore, random work …
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Systematic testing for actor programs
… is notoriously difficult, especially for shared-memory models of programming. The actor model of programming offers a promising alternative for developing concurrent systems based on message passing. In actor-based systems, shared-memory access is not allowed, and the key source of …
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WMM : a resilient Weak Memory Model
A good memory model should have a precise definition that can be understood by any computer architect readily. It should also be resilient in the sense that it should not break when new microarchitecture optimizations are introduced to improve single-threaded performance. We introduce WMM, a new …
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Long-memory stochastic volatility model calibration using deep neural nets
Widespread use of stochastic volatility models in the financial industry is bottlenecked by the complexity and intractability they present. Since the seminal work in quantitative finance by Black et al. and Merton, the infamous Black-Scholes model has been extensively used in the industry for …
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Learning to Forget: An Interference Account of Cue-Independent Forgetting
Memory suppression is investigated in inhibition paradigms that produce cue-independent forgetting. Because the forgotten items are not retrieved even when tested with an independent, semantically related cue, it has been assumed that this forgetting is due to an inhibition process. However, this …
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A no-thin-air memory model for programming languages
… runtime, etc. that make up the platform. A memory model, which prescribes what values each read of a concurrent program can read, allows programmers to determine whether a program behaves as expected without having to worry about the details of the platform. However, capturing these …
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