Data locality in high performance computing, big data, and converged systems: An analysis of the cutting edge and a future system architecture
Big data has revolutionized science and technology leading to the transformation of our
societies. High-performance computing (HPC) provides the necessary computational power …
societies. High-performance computing (HPC) provides the necessary computational power …
Machine learning in compiler optimization
In the last decade, machine-learning-based compilation has moved from an obscure
research niche to a mainstream activity. In this paper, we describe the relationship between …
research niche to a mainstream activity. In this paper, we describe the relationship between …
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 …
Learning intermediate representations using graph neural networks for numa and prefetchers optimization
There is a large space of NUMA and hardware prefetcher configurations that can
significantly impact the performance of an application. Previous studies have demonstrated …
significantly impact the performance of an application. Previous studies have demonstrated …
Modeling and optimizing numa effects and prefetching with machine learning
Both NUMA thread/data placement and hardware prefetcher configuration have significant
impacts on HPC performance. Optimizing both together leads to a large and complex design …
impacts on HPC performance. Optimizing both together leads to a large and complex design …
Compiler support for selective page migration in NUMA architectures
G Piccoli, HN Santos, RE Rodrigues, C Pousa… - Proceedings of the 23rd …, 2014 - dl.acm.org
Current high-performance multicore processors provide users with a non-uniform memory
access model (NUMA). These systems perform better when threads access data on memory …
access model (NUMA). These systems perform better when threads access data on memory …
Machine learning-based self-adjusting concurrency in software transactional memory systems
One of the problems of Software-Transactional-Memory (STM) systems is the performance
degradation that can be experienced when applications run with a non-optimal concurrency …
degradation that can be experienced when applications run with a non-optimal concurrency …
Data and thread placement in numa architectures: A statistical learning approach
Nowadays, NUMA architectures are common in compute-intensive systems. Achieving high
performance for multi-threaded application requires both a careful placement of threads on …
performance for multi-threaded application requires both a careful placement of threads on …
ZAKI+: A machine learning based process mapping tool for SpMV computations on distributed memory architectures
Smart cities and other cyber-physical systems (CPSs) rely on various scientific, engineering,
business, and social applications that provide timely intelligence for their design, operations …
business, and social applications that provide timely intelligence for their design, operations …
Mirencoder: Multi-modal ir-based pretrained embeddings for performance optimizations
A Dutta, A Jannesari - Proceedings of the 2024 International Conference …, 2024 - dl.acm.org
One of the primary areas of interest in High Performance Computing is the improvement of
performance of parallel workloads. Nowadays, compilable source code-based optimization …
performance of parallel workloads. Nowadays, compilable source code-based optimization …