Phase Change Memory (PCM) is emerging as an attractive alternative to Dynamic Random Access Memory (DRAM) in building data-intensive computing systems. PCM offers read/write performance asymmetry that makes it necessary to revisit the design of in-memory applications. In this paper, we focus on in-memory hash tables, a family of data structures with wide applicability. We evaluate several popular hash-table designs to understand their performance under PCM. We find that for write-heavy workloads the designs that achieve best performance for PCMdiffer from the ones that are best for DRAM, and that designs achieving a high load factor also cause a high number of memory writes. Finally, we propose PFHT, a PCM-Friendly Hash Table which presents a cuckoo hashing variant that is tailored to PCM characteristics, and offers a better trade-off between performance, the amount of writes generated, and the expected load factor than any of the existing DRAMbased implementations.
Mass transfer rates to the drops in liquid-liquid extraction equipment are often likely to be reduced by the presence of surface active contaminants. This reduction in mass transfer is said to be due to a reduction in terminal velocity, and to changes in pattern of internal circulation. A single-drop extraction apparatus was used to investigate the dependency of mass transfer coefficient on the amount of surfactant added in a system of n-butanol/succinic acid/water. Three types of surfactants, SDS, DTMAC and Triton X-100, were used to study their effects on the inhibition of mass transfer in liquid-liquid extraction. The effect of surfactants concentration on extraction percentage, overall mass transfer coefficient, and extra mass transfer resistance was investigated for these surfactants. Also variation of terminal velocity as a function of surfactant concentration and drop diameter were illustrated for both surfactants. Finally these surfactants were compared to each other.
We introduce new methods to replay intensive block I/O workloads more accurately. These methods can be used to reproduce realistic workloads for benchmarking, performance validation, and tuning of a high-performance block storage device/system. In this article, we study several sources in the stock operating system that introduce uncertainty in the workload replay. Based on the remedies of these findings, we design and develop a new replay tool called
hfplayer
that replays intensive block I/O workloads in a similar unscaled environment with more accuracy. To replay a given workload trace in a scaled environment with faster storage or host server, the dependency between I/O requests becomes crucial since the timing and ordering of I/O requests is expected to change according to these dependencies. Therefore, we propose a heuristic way of speculating I/O dependencies in a block I/O trace. Using the generated dependency graph,
hfplayer
tries to propagate I/O related performance gains appropriately along the I/O dependency chains and mimics the original application behavior when it executes in a scaled environment with slower or faster storage system and servers. We evaluate
hfplayer
with a wide range of workloads using several accuracy metrics and find that it produces better accuracy when compared to other replay approaches.
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