Using meta-heuristics and machine learning for software optimization of parallel computing systems: a systematic literature review
While modern parallel computing systems offer high performance, utilizing these powerful
computing resources to the highest possible extent demands advanced knowledge of …
computing resources to the highest possible extent demands advanced knowledge of …
A review of machine learning and meta-heuristic methods for scheduling parallel computing systems
Optimized software execution on parallel computing systems demands consideration of
many parameters at run-time. Determining the optimal set of parameters in a given …
many parameters at run-time. Determining the optimal set of parameters in a given …
Client-side reconstruction of composite mementos using serviceworker
We use the ServiceWorker (SW) API to intercept HTTP requests for embedded resources
and reconstruct Composite Mementos without the need for conventional URL rewriting …
and reconstruct Composite Mementos without the need for conventional URL rewriting …
Adaptive thread mapping strategies for transactional memory applications
Transactional Memory (TM) is a programmer friendly alternative to traditional lock-based
concurrency. Although it intends to simplify concurrent programming, the performance of the …
concurrency. Although it intends to simplify concurrent programming, the performance of the …
Adaptive load balancing based on machine learning for iterative parallel applications
The performance of irregular scientific applications can be easily affected by an uneven
distribution of work among the computing resources. In this context, Load Balancing (LB) …
distribution of work among the computing resources. In this context, Load Balancing (LB) …
Automatic skeleton-driven memory affinity for transactional worklist applications
Memory affinity has become a key element to achieve scalable performance on multi-core
platforms. Mechanisms such as thread scheduling, page allocation and cache prefetching …
platforms. Mechanisms such as thread scheduling, page allocation and cache prefetching …
Improving performance of transactional memory through machine learning
Y Xiao, T Jeyakumaran, E Atoofian… - Concurrency and …, 2018 - Wiley Online Library
Transactional memory (TM) is a programming paradigm that facilitates parallel programming
for multi‐core processors. In the last few years, some chip manufacturers provided hardware …
for multi‐core processors. In the last few years, some chip manufacturers provided hardware …
[PDF][PDF] Evaluating CPU and memory affinity for numerical scientific multithreaded benchmarks on multi-cores
CP Ribeiro, M Castro… - … Journal on Computer …, 2012 - iadisportal.org
Modern multi-core platforms feature complex topologies with different cache levels and
hierarchical memory subsystems. Consequently, thread and data placement become crucial …
hierarchical memory subsystems. Consequently, thread and data placement become crucial …
Communication‐aware thread mapping using the translation lookaside buffer
EHM Cruz, M Diener… - … and Computation: Practice …, 2015 - Wiley Online Library
Threads of parallel applications need to communicate in order to fulfill their tasks. The
communication performance between the cores in modern multi‐core architectures differs …
communication performance between the cores in modern multi‐core architectures differs …
Automatic optimization of software transactional memory through linear regression and decision tree
Y Xiao, Z Li, E Atoofian, A Jannesari - … 18-20, 2015, Proceedings, Part IV …, 2015 - Springer
Abstract Software Transactional Memory (STM) is a promising paradigm that facilitates
programming for shared memory multiprocessors. In STM, synchronization of accesses to …
programming for shared memory multiprocessors. In STM, synchronization of accesses to …