Exploiting NVM in large-scale graph analytics

Malicevic, Jasmina; Dulloor, Subramanya R.; Sundaram, Narayanan; Satish, Nadathur; Jackson, Jeff; Zwaenepoel, Willy

doi:10.1145/2819001.2819005

Cited by 27 publications

(6 citation statements)

References 29 publications

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“…Hybrid Memory Cube (HMC) [49,63], High Bandwidth Memory (HBM) [29]) and low-bandwidth, high-capacity memory (e.g. DDR, NVM [45]) are being widely adopted by vendors [27,57]. Recent work has investigated architecting high performance memory in a hybrid memory system either as a hardware-managed cache [68,76] or part of the OS-visible main memory system [10,27,51,75].…”

Section: Related Workmentioning

confidence: 99%

Nimble Page Management for Tiered Memory Systems

Yan

Lustig

Nellans

et al. 2019

Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Syst

102

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Software-controlled heterogeneous memory systems have the potential to increase the performance and cost efficiency of computing systems. However they can only deliver on this promise if supported by efficient page management policies and mechanisms within the operating system (OS). Current OS implementations do not support efficient tiering of data between heterogeneous memories. Instead, they rely on expensive offlining of memory or swapping data to disk as a means of profiling and migrating hot or cold data between memory nodes. They also leave numerous optimizations on the table; for example, multi-threaded hardware is not leveraged to maximize page migration throughput, resulting in up to 95% under-utilization of available memory bandwidth. To remedy these shortcomings, we propose and implement a general purpose OS-integrated multi-level memory management system that reuses current OS page tracking structures to tier pages directly between memories with no additional monitoring overhead. We augment this system with four additional optimizations: native support for transparent huge page migration, multi-threaded migration of a page, concurrent migration of multiple pages, and symmetric exchange of pages. Combined, these optimizations dramatically reduce kernel software overheads and improve raw page migration throughput over 15×. Implemented in Linux and evaluated on x86, Power, and ARM64 systems, our OS support for heterogeneous memories improves application performance 40% over baseline Linux for a suite of real-world memory-intensive workloads utilizing a multilevel disaggregated memory system.

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Section: Related Workmentioning

confidence: 99%

Nimble Page Management for Tiered Memory Systems

Yan

Lustig

Nellans

et al. 2019

Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Syst

102

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“…Phadke and Narayanasamy [8] also profile applications' MLP and LLC misses offline to determine from which memory space each application would benefit the most. There are also works require programmers to help decide data placement [13,19,20]. Our paper uses offline profiling for data placement instead of online monitoring and migration, and does not invoke extra burden to programmers.…”

Section: Related Workmentioning

confidence: 99%

Efficient Management for Hybrid Memory in Managed Language Runtime

Wang

Cao

Zigman

et al. 2016

Lecture Notes in Computer Science

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Hybrid memory, which leverages the benefits of traditional DRAM and emerging memory technologies, is a promising alternative for future main memory design. However popular management policies through memory-access recording and page migration may invoke non-trivial overhead in execution time and hardware space. Nowadays, managed language applications are increasingly dominant in every kind of platform. Managed runtimes provide services for automatic memory management. So it is important to adapt them for the underlying hybrid memory. This paper explores two opportunities, heap partition placement and object promotion, inside managed runtimes for allocating hot data in a fast memory space (fast-space) without any access recording or data migration overhead. For heap partition placement, we quantitatively analyze LLC miss density and performance effect for each partition. Results show that LLC misses especially store misses mostly hit nursery partitions. Placing nursery in fast-space, which is 20% total memory footprint of tested benchmarks on average, causes only 10% performance difference from all memory footprint in fast-space. During object promotion, hot objects will be directly allocated to fast-space. We develop a tool to analyze the LLC miss density for each method of workloads, since we have found that the LLC misses are mostly triggered by a small percentage of the total set of methods. The objects visited by the top-ranked methods are recognized as hot. Results show that hot objects do have higher access density, more than 3 times of random distribution for SPECjbb and pmd, and placing them in fast-space further reduces their execution time by 6% and 13% respectively.

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“…Malicevic et al 2015 [27], the authors partition performance-sensitive data into DRAM (that is, frequently accessed random data or critical write-only data) and the other data are placed in non-volatile memory technologies (NVM).…”

Section: Related Workmentioning

confidence: 99%

Learning to Rank Graph-based Application Objects on Heterogeneous Memories

Moura¹,

Petrucci²,

Mossé

2021

The International Symposium on Memory Systems

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Persistent Memory (PMEM), also known as Non-Volatile Memory (NVM), can deliver higher density and lower cost per bit when compared with DRAM. Its main drawback is that it is typically slower than DRAM. On the other hand, DRAM has scalability problems due to its cost and energy consumption. Soon, PMEM will likely coexist with DRAM in computer systems but the biggest challenge is to know which data to allocate on each type of memory. This paper describes a methodology for identifying and characterizing application objects that have the most influence on the application's performance using Intel Optane DC Persistent Memory. In the first part of our work, we built a tool that automates the profiling and analysis of application objects. In the second part, we build a machine learning model to predict the most critical object within large-scale graph-based applications. Our results show that using isolated features does not bring the same benefit compared to using a carefully chosen set of features. By performing data placement using our predictive model, we can reduce the execution time degradation by 12% (average) and 30% (max) when compared to the baseline's approach based on LLC misses indicator.

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Exploiting NVM in large-scale graph analytics

Cited by 27 publications

References 29 publications

Nimble Page Management for Tiered Memory Systems

Nimble Page Management for Tiered Memory Systems

Efficient Management for Hybrid Memory in Managed Language Runtime

Learning to Rank Graph-based Application Objects on Heterogeneous Memories

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