Date of Award
2019
Degree Type
Dissertation
Degree Name
Doctor of Philosophy in Electrical Engineering
Department
Electrical, Computer, and Biomedical Engineering
First Advisor
Resit Sendag
Abstract
Hardware predictors are widely used to improve the performance of modern processors. These predictors are mostly used in data or instruction prefetching mechanisms and branch predictors. Hardware-based prefetchers and branch predictors can work dynamically based on the program's run-time behavior. However, most of the hardware-based predictor mechanisms depend on detecting patterns (data access patterns, branch patterns, etc) and they require very complex mechanisms to be able to capture irregular patterns.
Software techniques, like software prefetching, can help to improve the performance of the applications with behaviors that are difficult to capture by hardware mechanisms. On the other hand, they mostly rely on execution special instructions repeatedly during the execution which is likely to create an instruction overhead. Also, they cannot respond to the run-time behaviors like hardware mechanisms.
To overcome the weaknesses of prediction mechanisms, we proposed mechanisms which combine the strengths of hardware and software mechanisms. In this thesis, we examined ways to use the knowledge we can extract from the software to inform hardware mechanisms. We enable hardware-based systems to capture complex software behaviors just using the information it receives from the software instead of using large history tables and buffers to try to make predictions.
First, we proposed our hardware-based prefetching mechanism called Sequential Prefetcher with Adaptive Distance (SPAD). SPAD uses a simple method of hardware prefetching that integrates timeliness into sequential prefetching. It can outperform recently proposed complex prefetchers with simpler and smaller hardware.
In the second chapter, we proposed a software supported hardware prefetching mechanism called Array Tracking Prefether (ATP). This mechanism targets irregular memory access patterns and relies on compiler/programmer to configure hardware prefetching mechanism. By combining the strength of software and hardware methods, ATP outperforms proposed software only and hardware-only solutions.
Finally, we proposed another software-assisted prefetcher for pointer intensive in-memory database applications, Node Tracker (NT). Although NT is proposed as a prefetcher, it is capable of using the knowledge it extracts from the prefetched data to help CPU pipeline in other ways to increase throughput. While tightly integrated with CPU, NT can achieve up to 19x speedup for the targeted applications.
Recommended Citation
Çavus, Mustafa, "SOFTWARE SUPPORT FOR HARDWARE PREDICTORS" (2019). Open Access Dissertations. Paper 896.
https://digitalcommons.uri.edu/oa_diss/896
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