Data Prefetching Techniques in Computer Systems

Data Prefetching Techniques in Computer Systems,Volume 125 provides an in-depth review of the latest progress on data prefetching research, covering temporal, spatial, non-spatial-temporal, and evaluation prefetchers, with insights on future research directions.

Format: Hardback
Length: 102 pages
Publication date: 28 February 2022
Publisher: Elsevier Science & Technology

Data prefetching techniques are crucial in enhancing the performance of computer systems by predicting and fetching data that is likely to be accessed in the future. This volume, Data Prefetching Techniques in Computer Systems, Volume 125, offers a comprehensive exploration of the latest advancements in this field. It delves into various types of prefetching techniques, including temporal prefetchers, spatial prefetchers, non-spatial-temporal prefetchers, and their evaluation. The book provides insights into potential future research directions and highlights specific chapters covering topics such as an introduction to data prefetching, spatial prefetching techniques, temporal prefetching techniques, the Domino prefetching scheme, the Bingo prefetching method, and the Champion prefetcher.

Temporal prefetching involves predicting and fetching data that is likely to be accessed soon based on past behavior or patterns. This technique can significantly improve system performance by reducing the time taken to access frequently used data. Spatial prefetching, on the other hand, focuses on predicting and fetching data that is physically close to the processor or memory that will be used to access it. This technique can reduce the physical distance between the data and the processor, resulting in faster data access times. Non-spatial-temporal prefetching combines temporal and spatial prefetching techniques to predict and fetch data that is both time- and space-sensitive. This technique can enhance system performance by taking into account both temporal and spatial dependencies.

Evaluation of prefetchers is an important aspect of data prefetching research. Various evaluation metrics and methods have been developed to assess the effectiveness of prefetching techniques. These metrics include hit rate, miss rate, and average access time. Hit rate measures the percentage of times that a prefetched item is actually accessed, while miss rate measures the percentage of times that a prefetched item is not accessed. Average access time measures the average time taken to access a prefetched item.

The book also discusses the development of new prefetching techniques and algorithms. Some of the emerging techniques include predictive caching, multi-level caching, and cooperative prefetching. Predictive caching involves predicting the future access patterns of data and prefetching data based on these predictions. Multi-level caching involves using multiple levels of cache memory to store prefetched data. Cooperative prefetching involves coordinating prefetching activities between multiple components of a system, such as the processor, memory, and storage.

In conclusion, Data Prefetching Techniques in Computer Systems, Volume 125 provides a comprehensive and up-to-date review of the latest progress in data prefetching research. The book covers various types of prefetching techniques, their evaluation, and potential future research directions. It is an invaluable resource for researchers, practitioners, and students interested in enhancing the performance of computer systems through data prefetching.

Weight: 450g
Dimension: 229 x 152 (mm)
ISBN-13: 9780323851190