Sökning: "Memory consistency"
Visar resultat 16 - 20 av 28 avhandlingar innehållade orden Memory consistency.
16. Shared Memory Objects as Synchronization Abstractions: Algorithmic Implementations and Concurrent Applications
Sammanfattning : Multicore and many-core architectures have penetrated the vast majority of computing systems, from high-end servers to low-energy embedded devices. From the hardware's perspective, performance scalability comes in the form of increasing numbers of cores. Nevertheless, fully utilizing this power is still an open research and engineering issue. LÄS MER
17. Evaluation of design alternatives for a directory-based cache coherence protocol in shared-memory multiprocessors
Sammanfattning : In shared-memory multiprocessors, caches are attached to the processors in order to reduce the memory access latency. To keep the memory consistent, a cache coherence protocol is needed. LÄS MER
18. Pattern-based Specification and Formal Analysis of Embedded Systems Requirements and Behavioral Models
Sammanfattning : Since the first lines of code were introduced in the automotive domain, vehicles have transitioned from being predominantly mechanical systems to software intensive systems. With the ever-increasing computational power and memory of vehicular embedded systems, a set of new, more powerful and more complex software functions are installed into vehicles to realize core functionalities. LÄS MER
19. Efficient Execution Paradigms for Parallel Heterogeneous Architectures
Sammanfattning : This thesis proposes novel, efficient execution-paradigms for parallel heterogeneous architectures. The end of Dennard scaling is threatening the effectiveness of DVFS in future nodes; therefore, new execution paradigms are required to exploit the non-linear relationship between performance and energy efficiency of memory-bound application-regions. LÄS MER
20. Adaptiveness and Lock-free Synchronization in Parallel Stochastic Gradient Descent
Sammanfattning : The emergence of big data in recent years due to the vast societal digitalization and large-scale sensor deployment has entailed significant interest in machine learning methods to enable automatic data analytics. In a majority of the learning algorithms used in industrial as well as academic settings, the first-order iterative optimization procedure Stochastic gradient descent (SGD), is the backbone. LÄS MER