abstract: One of the main goals of computer architecture design is to improve performance without much increase in the power consumption. It cannot be achieved by adding increasingly complex intelligent schemes in the hardware, since they will become increasingly less power-efficient. Therefore, parallelism comes up as the solution. In fact, the irrevocable trend of computer design in near future is still to keep increasing the number of cores while reducing the operating frequency. However, it is not easy to scale number of cores. One important challenge is that existing cores consume too much power. Another challenge is that cache-based memory hierarchy poses a serious limitation due to the rapidly increasing demand of area and power for coherence maintenance.
In this dissertation, opportunities to resolve the aforementioned issues were explored in two aspects.
Firstly, the possibility of removing hardware cache altogether, and replacing it with scratchpad memory with software management was explored. Scratchpad memory consumes much less power than caches. However, as data management logic is completely shifted to Software, how to reduce software overhead is challenging. This thesis presents techniques to manage scratchpad memory judiciously by exploiting application semantics and knowledge of data access patterns, thereby enabling optimization of data movement across the memory hierarchy. Experimental results show that the optimization was able to reduce stack data management overhead by 13X, produce better code mapping in more than 80% of the case, and improve performance by 83% in heap management.
Secondly, the possibility of using software branch hinting to replace hardware branch prediction to completely eliminate power consumption on corresponding hardware components was explored. As branch predictor is removed from hardware, software logic is responsible for reducing branch penalty. Techniques to minimize the branch penalty by optimizing branch hint placement were proposed, which can reduce branch penalty by 35.4% over the state-of-the-art. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2019
| Identifer | oai:union.ndltd.org:asu.edu/item:53524 |
| Date | January 2019 |
| Contributors | Lu, Jing (Author), Shrivastava, Aviral (Advisor), Sarjoughian, Hessam (Committee member), Wu, Carole-Jean (Committee member), Doupe, Adam (Committee member), Arizona State University (Publisher) |
| Source Sets | Arizona State University |
| Language | English |
| Detected Language | English |
| Type | Doctoral Dissertation |
| Format | 133 pages |
| Rights | http://rightsstatements.org/vocab/InC/1.0/ |
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