Eucalyptus: Support for effective use of persistent memory
Mohammad Banikazemi, Bulent Abali
IPDPSW 2012
Lossless data compression is highly desirable in enterprise and cloud environments for storage and memory cost savings and improved utilization I/O and network. While the value provided by compression is recognized, its application in practice is often limited because it's a processor intensive operation resulting low throughput and high elapsed time for compression intense workloads. The IBM POWER9 and IBM z15 systems overcome the shortcomings of existing approaches by including a novel on-chip integrated data compression accelerator. The accelerator reduces processor cycles, I/O traffic, memory and storage footprint of many applications practically with zero hardware cost. The accelerator also eliminates the cost and I/O slots that would have been necessary with FPGA/ASIC based compression adapters. On the POWER9 chip, a single accelerator uses less than 0.5% of the processor chip area, but provides a 388x speedup factor over the zlib compression software running on a general-purpose core and provides a 13x speedup factor over the entire chip of cores. On a POWER9 system, the accelerators provide an end-to-end 23% speedup to Apache Spark TPC-DS workload compared to the software baseline. The z15 chip doubles the compression rate of POWER9 resulting in even much higher speedup factors over the compression software running on general-purpose cores. On a maximally configured z15 system topology, on-chip compression accelerators provide up to 280 GB/s data compression rate, the highest in the industry. Overall, the on-chip accelerators significantly advance the state of the art in terms of area, throughput, latency, compression ratio, reduced processor utilization, power/energy efficiency, and integration into the system stack. This paper describes the architecture, and novel elements of the POWER9 and z15 compression/decompression accelerators with emphasis on trade-offs that made the on-chip implementation possible.
Mohammad Banikazemi, Bulent Abali
IPDPSW 2012
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ICS 2009
Nagadastagiri Challapalle, Sahithi Rampalli, et al.
ISCA 2020
I-Hsin Chung, Bulent Abali, et al.
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