Polymorphic Memory: A Hybrid Approach for Utilizing On-Chip Memory in Manycore Systems

• Published in: Electronics (Basel) 2020-12, Vol.9 (12), p.2061
• Main Authors: Lim, Seung-Ho, Seok, Hyunchul, Park, Ki-Woong
• Format: Article
• Language: English
• Subjects: Allocations; Bandwidths; Benchmarks; Computer architecture; Design; Dynamic random access memory; Hierarchies; Performance enhancement; Performance evaluation; Random access memory; Semiconductors
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics9122061

The key challenges of manycore systems are the large amount of memory and high bandwidth required to run many applications. Three-dimesnional integrated on-chip memory is a promising candidate for addressing these challenges. The advent of on-chip memory has provided new opportunities to rethink traditional memory hierarchies and their management. In this study, we propose a polymorphic memory as a hybrid approach when using on-chip memory. In contrast to previous studies, we use the on-chip memory as both a main memory (called M1 memory) and a Dynamic Random Access Memory (DRAM) cache (called M2 cache). The main memory consists of M1 memory and a conventional DRAM memory called M2 memory. To achieve high performance when running many applications on this memory architecture, we propose management techniques for the main memory with M1 and M2 memories and for polymorphic memory with dynamic memory allocations for many applications in a manycore system. The first technique is to move frequently accessed pages to M1 memory via hardware monitoring in a memory controller. The second is M1 memory partitioning to mitigate contention problems among many processes. Finally, we propose a method to use M2 cache between a conventional last-level cache and M2 memory, and we determine the best cache size for improving the performance with polymorphic memory. The proposed schemes are evaluated with the SPEC CPU2006 benchmark, and the experimental results show that the proposed approaches can improve the performance under various workloads of the benchmark. The performance evaluation confirms that the average performance improvement of polymorphic memory is 21.7%, with 0.026 standard deviation for the normalized results, compared to the previous method of using on-chip memory as a last-level cache.