Affiliation:
1. Indian Institute of Technology, Madras
2. Indian Institute of Technology, Kanpur
Abstract
Compression techniques at the last-level cache and the DRAM play an important role in improving system performance by increasing their effective capacities. A compressed block in DRAM also reduces the transfer time over the memory bus to the caches, reducing the latency of a LLC cache miss. Usually, compression is achieved by exploiting data patterns present within a block. But applications can exhibit data locality that spread across multiple consecutive data blocks. We observe that there is significant opportunity available for compressing multiple consecutive data blocks into one single block, both at the LLC and DRAM. Our studies using 21 SPEC CPU applications show that, at the LLC, around 25% (on average) of the cache blocks can be compressed into one single cache block when grouped together in groups of 2 to 8 blocks. In DRAM, more than 30% of the columns residing in a single DRAM page can be compressed into one DRAM column, when grouped together in groups of 2 to 6. Motivated by these observations, we propose a mechanism, namely, MBZip, that compresses multiple data blocks into one single block (called a zipped block), both at the LLC and DRAM. At the cache, MBZip includes a simple tag structure to index into these zipped cache blocks and the indexing does not incur any redirectional delay. At the DRAM, MBZip does not need any changes to the address computation logic and works seamlessly with the conventional/existing logic. MBZip is a synergistic mechanism that coordinates these zipped blocks at the LLC and DRAM. Further, we also explore silent writes at the DRAM and show that certain writes need not access the memory when blocks are zipped. MBZip improves the system performance by 21.9%, with a maximum of 90.3% on a 4-core system.
Publisher
Association for Computing Machinery (ACM)
Subject
Hardware and Architecture,Information Systems,Software
Cited by
8 articles.
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1. FlatPack;Proceedings of the International Conference on Parallel Architectures and Compilation Techniques;2022-10-08
2. L
2
C: Combining Lossy and Lossless Compression on Memory and I/O;ACM Transactions on Embedded Computing Systems;2022-01-14
3. FlitZip: Effective Packet Compression for NoC in MultiProcessor System-on-Chip;IEEE Transactions on Parallel and Distributed Systems;2022-01-01
4. MemSZ;ACM Transactions on Architecture and Code Optimization;2020-12-31
5. Compacted CPU/GPU Data Compression via Modified Virtual Address Translation;Proceedings of the ACM on Computer Graphics and Interactive Techniques;2020-08-26