Cache Reuse Aware Replacement Policy for Improving GPU Cache Performance

Embedded systems are designed for a variety of applications ranging from Hard Real Time applications to mobile computing, which demands various types of cache designs for better performance. Since real-time applications place stringent requirements on performance, the role of the cache subsystem assumes significance. Reconfigurable caches meet performance requirements under this context. Existing reconfigurable caches tend to use associativity and size for maximizing cache performance. This article proposes a novel approach of a reconfigurable and intelligent data cache (L1) based on replacement algorithms. An intelligent embedded data cache and a dynamic reconfigurable intelligent embedded data cache have been implemented using Verilog 2001 and tested for cache performance. Data collected by enabling the cache with two different replacement strategies have shown that the hit rate improves by 40% when compared to LRU and 21% when compared to MRU for sequential applications which will significantly improve performance of embedded real time application.

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Modeling cache performance for embedded systems

Bulletin of Electrical Engineering and Informatics ◽

10.11591/eei.v10i5.2459 ◽

2021 ◽

Vol 10 (5) ◽

pp. 2910-2920

Author(s):

Ogechukwu Kingsley Ugwueze ◽

Chijindu C. V. ◽

Udeze C. C. ◽

Ahaneku A. M. ◽

Eneh N. J. ◽

...

Keyword(s):

Embedded Systems ◽

Performance Model ◽

Replacement Policy ◽

Cumulative Probability ◽

Estimation Model ◽

Cache Performance ◽

The Mean ◽

Design Options ◽

Cache Design ◽

Cache Configurations

This paper presents a cache performance model for embedded systems. The need for efficient cache design in embedded systems has led to the exploration of various methods of design for optimal cache configurations for embedded processor. Better users’ experiences are realized by improving performance parameters of embedded systems. This work presents a cache hit rate estimation model for embedded systems that can be used to explore optimal cache configurations using Bourneli’s binomial cumulative probability based on application of reuse distance profiles. The model presented was evaluated using three mibench benchmarks which are bitcount, basicmath and FFT for 4kb, 8kb, 16kb, 32kb and 64kb sizes of cache under 2-way, 4-ways, 8-ways and 16-ways set associative configurations, all using least recently-used (LRU) replacement policy. The results were compared with the results obtained using sim-cheetah from simplescalar simulators suite. The mean errors for bitcount, basicmath, and FFT benchmarks are 0.0263%, 2.4476%, and 1.9000% respectively. Therefore, the mean error for the three benchmarks is equal to 1.4579%. The margin of errors in the results was below 5% and within the acceptable limits showing that the model can be used to estimate hit rates of cache and to explore cache design options.

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An efficient Intelligent Cache Replacement Policy Suitable for PACS

International Journal of Machine Learning and Computing ◽

10.18178/ijmlc.2021.11.3.1043 ◽

2021 ◽

Vol 11 (3) ◽

pp. 250-255

Author(s):

Yinyin Wang ◽

◽

Yuwang Yang ◽

Qingguang Wang

Keyword(s):

Communication Systems ◽

Replacement Policy ◽

Support Vector ◽

Cache Replacement ◽

Cache Performance ◽

File Access ◽

Cache Replacement Policy ◽

Picture Archiving And Communication ◽

Intelligent Cache ◽

Cache Replacement Policies

An efficient intelligent cache replacement policy suitable for picture archiving and communication systems (PACS) was proposed in this work. By combining the Support vector machine (SVM) with the classic least recently used (LRU) cache replacement policy, we have created a new intelligent cache replacement policy called SVM-LRU. The SVM-LRU policy is unlike conventional cache replacement policies, which are solely dependent on the intrinsic properties of the cached items. Our PACS-oriented SVM-LRU algorithm identifies the variables that affect file access probabilities by mining medical data. The SVM algorithm is then used to model the future access probabilities of the cached items, thus improving cache performance. Finally, a simulation experiment was performed using the trace-driven simulation method. It was shown that the SVM-LRU cache algorithm significantly improves PACS cache performance when compared to conventional cache replacement policies like LRU, LFU, SIZE and GDS.

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