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LightPC Presents a Resilient System Using Only Non-Volatile Memory
Lightweight Persistence Centric System (LightPC) ensures both data and execution persistence for energy-efficient full system persistence A KAIST research team has developed hardware and software technology that ensures both data and execution persistence. The Lightweight Persistence Centric System (LightPC) makes the systems resilient against power failures by utilizing only non-volatile memory as the main memory. “We mounted non-volatile memory on a system board prototype and created an operating system to verify the effectiveness of LightPC,” said Professor Myoungsoo Jung. The team confirmed that LightPC validated its execution while powering up and down in the middle of execution, showing up to eight times more memory, 4.3 times faster application execution, and 73% lower power consumption compared to traditional systems. Professor Jung said that LightPC can be utilized in a variety of fields such as data centers and high-performance computing to provide large-capacity memory, high performance, low power consumption, and service reliability. In general, power failures on legacy systems can lead to the loss of data stored in the DRAM-based main memory. Unlike volatile memory such as DRAM, non-volatile memory can retain its data without power. Although non-volatile memory has the characteristics of lower power consumption and larger capacity than DRAM, non-volatile memory is typically used for the task of secondary storage due to its lower write performance. For this reason, nonvolatile memory is often used with DRAM. However, modern systems employing non-volatile memory-based main memory experience unexpected performance degradation due to the complicated memory microarchitecture. To enable both data and execution persistent in legacy systems, it is necessary to transfer the data from the volatile memory to the non-volatile memory. Checkpointing is one possible solution. It periodically transfers the data in preparation for a sudden power failure. While this technology is essential for ensuring high mobility and reliability for users, checkpointing also has fatal drawbacks. It takes additional time and power to move data and requires a data recovery process as well as restarting the system. In order to address these issues, the research team developed a processor and memory controller to raise the performance of non-volatile memory-only memory. LightPC matches the performance of DRAM by minimizing the internal volatile memory components from non-volatile memory, exposing the non-volatile memory (PRAM) media to the host, and increasing parallelism to service on-the-fly requests as soon as possible. The team also presented operating system technology that quickly makes execution states of running processes persistent without the need for a checkpointing process. The operating system prevents all modifications to execution states and data by keeping all program executions idle before transferring data in order to support consistency within a period much shorter than the standard power hold-up time of about 16 minutes. For consistency, when the power is recovered, the computer almost immediately revives itself and re-executes all the offline processes immediately without the need for a boot process. The researchers will present their work (LightPC: Hardware and Software Co-Design for Energy-Efficient Full System Persistence) at the International Symposium on Computer Architecture (ISCA) 2022 in New York in June. More information is available at the CAMELab website (http://camelab.org). -Profile: Professor Myoungsoo Jung Computer Architecture and Memory Systems Laboratory (CAMEL)http://camelab.org School of Electrical EngineeringKAIST
Professor Minsoo Rhu Recognized as Facebook Research Scholar
Professor Minsoo Rhu from the School of Electrical Engineering was selected as the recipient of the Systems for Machine Learning Research Awards presented by Facebook. Facebook launched the award last year with the goal of funding impactful solutions in the areas of developer tookits, compilers and code generation, system architecture, memory technologies, and machine learning accelerator support. A total of 167 scholars from 100 universities representing 26 countries submitted research proposals, and Facebook selected final 10 scholars. Professor Rhu made the list with his research topic ‘A Near-Memory Processing Architecture for Training Recommendation Systems.’ He will receive 5,000 USD in research funds at the award ceremony which will take place during this year’s AI Systems Faculty Summit at the Facebook headquarters in Menlo Park, California. Professor Rhu’s submission was based on research on ‘Memory-Centric Deep Learning System Architecture’ that he carried out for three years under the auspices of Samsung Science and Technology Foundation from 2017. It was an academic-industrial cooperation research project in which leading domestic companies like Samsung Electronics and SK Hynix collaborated to make a foray into the global memory-centric smart system semiconductor market. Professor Rhu who joined KAIST in 2018 has led various systems research projects to accelerate the AI computing technology while working at NVIDIA headquarters from 2014. (END)
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