| 1. |
- Angerd, Alexandra, 1988, et al.
(författare)
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GBDI: Going Beyond Base-Delta-Immediate Compression with Global Bases
- 2022
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Ingår i: Proceedings - International Symposium on High-Performance Computer Architecture. - 1530-0897. - 9781665420273 ; 2022-April, s. 1115-1127
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Konferensbidrag (refereegranskat)abstract
- Memory bandwidth is limiting performance for many emerging applications. While compression techniques can unlock a higher memory bandwidth, prior art offers only modestly better bandwidth. This paper contributes with a new compression method - Global Base Delta Immediate compression (GBDI) - that offers substantially higher memory bandwidth by, unlike prior art, selecting base values across memory blocks. GBDI uses a novel clustering algorithm through data analysis in the background. The presented accelerator infrastructure offers low area overhead and latency. This paper shows that GBDI offers a compression ratio of 2.3×, and yields 1.5× higher bandwidth and 1.1× higher performance compared with a baseline without compression support, on average, for SPEC2017 benchmarks requiring medium to high memory bandwidth.
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| 2. |
- Eldstål-Ahrens, Albin, 1988, et al.
(författare)
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FlatPack: Flexible Compaction of Compressed Memory
- 2022
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Ingår i: Parallel Architectures and Compilation Techniques - Conference Proceedings, PACT. - New York, NY, USA : ACM. - 1089-795X. ; , s. 96-108
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Konferensbidrag (refereegranskat)abstract
- The capacity and bandwidth of main memory is an increasingly important factor in computer system performance. Memory compression and compaction have been combined to increase effective capacity and reduce costly page faults. However, existing systems typically maintain compaction at the expense of bandwidth. One major cause of extra traffic in such systems is page overflows, which occur when data compressibility degrades and compressed pages must be reorganized. This paper introduces FlatPack, a novel approach to memory compaction which is able to mitigate this overhead by reorganizing compressed data dynamically with less data movement. Reorganization is carried out by an addition to the memory controller, without intervention from software. FlatPack is able to maintain memory capacity competitive with current state-of-the-art memory compression designs, while reducing mean memory traffic by up to 67%. This yields average improvements in performance and total system energy consumption over existing memory compression solutions of 31-46% and 11-25%, respectively. In total, FlatPack improves on baseline performance and energy consumption by 108% and 40%, respectively, in a single-core system, and 83% and 23%, respectively, in a multi-core system.
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| 3. |
- Eldstål-Ahrens, Albin, 1988, et al.
(författare)
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L2C: Combining Lossy and Lossless Compression on Memory and I/O
- 2022
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Ingår i: Transactions on Embedded Computing Systems. - : Association for Computing Machinery (ACM). - 1558-3465 .- 1539-9087. ; 21:1
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Tidskriftsartikel (refereegranskat)abstract
- In this paper we introduce L2C, a hybrid lossy/lossless compression scheme applicable both to the memory subsystem and I/O traffic of a processor chip. L2C employs general-purpose lossless compression and combines it with state of the art lossy compression to achieve compression ratios up to 16:1 and improve the utilization of chip's bandwidth resources. Compressing memory traffic yields lower memory access time, improving system performance and energy efficiency. Compressing I/O traffic offers several benefits for resource-constrained systems, including more efficient storage and networking. We evaluate L2C as a memory compressor in simulation with a set of approximation-tolerant applications. L2C improves baseline execution time by an average of 50\%, and total system energy consumption by 16%. Compared to the lossy and lossless current state of the art memory compression approaches, L2C improves execution time by 9% and 26% respectively, and reduces system energy costs by 3% and 5%, respectively. I/O compression efficacy is evaluated using a set of real-life datasets. L2C achieves compression ratios of up to 10.4:1 for a single dataset and on average about 4:1, while introducing no more than 0.4% error.
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| 4. |
- Shao, Qi, 1991, et al.
(författare)
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HMComp: Extending Near-Memory Capacity using Compression in Hybrid Memory
- 2024
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Ingår i: Proceedings of the International Conference on Supercomputing. ; , s. 74-84
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Konferensbidrag (refereegranskat)abstract
- Hybrid memories, especially combining a first-tier near memory using High-Bandwidth Memory (HBM) and a second-tier far memory using DRAM, can realize a large and low cost, high-bandwidth main memory. State-of-the-art hybrid memories typically use a flat hierarchy where blocks are swapped between near and far memory based on bandwidth demands. However, this may cause significant overheads for metadata storage and traffic. While using a fixed-size, near-memory cache and compressing data in near memory can help, precious near-memory capacity is still wasted by the cache and the metadata needed to manage a compressed hybrid memory. This paper proposes HMComp, a flat hybrid-memory architecture, in which compression techniques free up near-memory capacity to be used as a cache for far memory data to cut down swap traffic without sacrificing any memory capacity. Moreover, through a carefully crafted metadata layout, we show that metadata can be stored in less costly far memory, thus avoiding to waste any near-memory capacity. Overall, HMComp offers a speedup of single-thread performance of up to 22%, on average 13%, and traffic reduction due to swapping of up to 60% and by 41% on average compared to flat hybrid memory designs.
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