Influenza virus (Flu) and respiratory syncytial virus (RSV) are the primary pathogens responsible for acute respiratory infections. Both viruses are prone to mutations due to the seasonal epidemic, leading to an increasing rate of false-negative results. In this study, comprehensive meta-analyses of the genomes focusing on most conserved fragments have been performed for the four seasonal influenza viruses (two subtypes of Flu A: H1N1 and H3N2; two subtypes of Flu B: Yamagata and Victoria) and the two types of RSV: RSVA and RSVB), respectively. The most conserved sequences of 200 bp were identified as targets of the designed primer/probe sets for RT-qPCR were screened and optimized. Good sensitivities of the optimized primer/probe sets were obtained with the limits of detections of 2.95, 2.82, 1.57, 2.8, 1.19, and 2.12 copies/reaction for H1N1, H3N2, Yamagata, Victoria, RSVA and RSVB, respectively. Eventually, quadruplex qPCR using the four designed primer/probe sets can achieve simultaneous screening of the four viruses at a single tube. Furthermore, the assay's good performance in detecting target viruses from clinical throat swab samples demonstrated its potential for diagnosis of these viruses. The method, based on the identified conserved sequences and primer/probe sets, can effectively reduce false-negative results and rapidly respond to these viruses during respiratory disease outbreaks, or even before their widespread emergence, which aid in preventing outbreaks and guiding clinical treatment.

We present AugeFS, a scalable userspace log-structured file system for modern SSDs. AugeFS re-architects the file system stack to address three critical challenges: inefficient control plane, limited metadata scalability, and underutilized device bandwidth. First, we propose a shared and protected address space within the userspace of accessing applications to run AugeFS, which enables the high-performance data plane and efficient control plane. Second, we design a scalable LSM-tree based key-value store called MetaDB to organize small-sized metadata in AugeFS. To improve the metadata scalability, MetaDB employs parallel request processing to reduce thread synchronization overhead and fine-grained parallel write-ahead log to eliminate false sharing in metadata persistence. Finally, AugeFS distributes files into different domains. To reduce contention, we maintain space management metadata for each domain independently, which helps scale data performance and improve the device IO utilization. Moreover, AugeFS designs an asynchronous IO stack for fsync to reduce the latency of synchronous writes. The evaluation results show that AugeFS significantly improves both metadata scalability and data scalability.

Persistent memory (PM) allows file systems to directly persist data on the memory bus. To increase the capacity of PM file systems, building a file system across sockets with each attached PM is attractive. However, accessing data across sockets incurs impacts of the non-uniform memory access (NUMA) architecture, which will lead to significant performance degradation. In this paper, we first use experiments to understand the NUMA impacts on building PM file systems. And then, we propose four design principles for building a high-performance PM file system NapFS for the NUMA architecture. We architect NapFS with per-socket local PM file systems and per-socket dedicated IO thread pools. This not only allows applications to delegate data accesses to IO threads for avoiding remote PM accesses, but also fully reuses existing single-socket PM file systems to reduce implementation complexity. Additionally, NapFS utilizes fast DRAM to accelerate performance by adding a global cache and adopts a selective cache mechanism to eliminate the redundant double-copy overhead for synchronization operations. Lastly, we show that NapFS can adopt extended optimizations to improve scalability and the performance of critical requests. We evaluate NapFS against other multi-socket PM file systems. The evaluation results show that NapFS achieves 2.2x and 1.0x throughput improvement for Filebench and RocksDB, respectively.

Nowadays, data centers consolidate latency-critical (LC) tenants and best-effort (BE) tenants on the same cloud platform to increase resource utilization and reduce costs. In such a scenario, the underlying distributed storage systems are responsible for guaranteeing SLOs for LC tenants while maximizing bandwidth for BE tenants. As high-performance NVMe SSDs are widely deployed, how to make full use of their performance capabilities and guarantee SLOs has become an urgent problem. However, current methods restrict the performance capabilities of NVMe SSDs based on a conservative offline model, and also ignore runtime changes in tenant loads and device states, which definitely affect the performance capabilities. In this paper, we present zQoS, an efficient technique that unleashes full performance capabilities of NVMe SSDs, and increases bandwidth of BE tenants while guaranteeing SLOs of LC tenants. First, zQoS builds a more accurate offline performance model for NVMe SSDs to accurately reflect their performance characteristics. Second, a fine-grained online adjustment mechanism is proposed to dynamically adjust the performance capabilities of NVMe SSDs at runtime. Finally, to cope with abrupt load changes, an adaptive per-tenant adjustment method is designed to guarantee SLOs and increase utilization. We evaluate zQoS in a wide variety of mixed workload scenarios. Results show that zQoS significantly outperforms the state-of-the-art approaches. It achieves up to a 17x increase in BE tenant bandwidth without violating LC tenant SLOs.

Memory disaggregation decouples CPUs and memory in monolithic servers to form compute nodes (CNs) and memory nodes (MNs) for elastic and efficient memory scaling. Memory disaggregation benefits in-memory key-value stores (KVSs) that demand large memory capacity. Building a KVS with full functionality, low latency, and high space efficiency under memory disaggregation is critically required by real-world applications. However, we observe that existing disaggregated KVSs fail to achieve all the above demands simultaneously. In this paper, we present DiStore, a full-disaggregation-friendly KVS that accomplishes the above goals. We carefully divide the responsibilities of CNs and MNs when involved in index traversing, concurrency control, and memory management. We first design a two-layer indexing structure, separable adaptive linked array, to reduce network RTTs for index traversing and improve space efficiency. Then, DiStore introduces thread-context-based concurrency control to enable inter-thread collaborating to reduce stall time under multi-thread contention on CNs. Finally, we propose cachable disaggregated memory management to allow CNs to manage remote memory locally with marginal space overhead. We implement DiStore and the evaluation shows that DiStore can reduce P999 tail latency by up to 72.1% and improve space efficiency by up to 33.6%. Meanwhile, DiStore achieves comparable throughput as state-of-the-art disaggregated KVSs.

Memory-mapped IO offers several advantages over explicit read/write IO. It requires no system call, incurs minimal overhead in case of cache hits, and avoids extra data copies between user and kernel space. However, we still identify inefficiencies in current memory-mapped IO designs when meeting fast storage devices: i) the heavy IO stack in the page fault handler, ii) the suboptimal prefetching design, and iii) the inefficient eviction policy. To address these limitations, we present SuperMap, an alternative design for the memory-mapped IO in Linux, which specifically brings high performance and flexibility for fast devices. First, SuperMap designs a lightweight and asynchronous IO stack by directly accessing device, reducing software overhead significantly. Second, SuperMap introduces a fine-grained and application-customized prefetcher framework based on eBPF, further improving performance. Third, SuperMap proposes a hotness-aware eviction policy with the hardware assistance, trying to keep frequently accessed data in memory. Through evaluations using benchmarks and real-world applications, we demonstrate that SuperMap outperforms the state-of-the-art memory-mapped IO design (FastMap) up to 67%.

Persistent memory (PM) promises byte-addressability, large capacity, and durability. Main memory systems, such as key-value stores and in-memory databases, benefit from such features of PM. Due to the great popularity of hashing index in main memory systems, a number of research efforts are made to provide high average performance persistent hashing. However, suboptimal tail performance in terms of tail throughput and tail latency is still observed for existing persistent hashing. In this paper, we analyze major sources of suboptimal tail performance from key design issues of persistent hashing. We identify the global hash structure and concurrency control as remaining explorable design spaces for improving tail performance. We propose Directory-sharing Multi-level Extendible Hashing (Dalea) for PM. Dalea designs ancestor link-based extendible hashing as well as fine-grained transient lock to address the two main sources (rehashing and locking) affecting tail performance. The evaluation results show that, compared with state-of-the-art persistent hashing Dash, Dalea achieves increased tail throughput by 4.1x and reduced tail latency by 5.4x. Moreover, in order to provide design guidelines for improving tail performance, we adopt Dalea as a testbed to identify different impacts of four factors on tail performance, including fine-grained rehashing, transient locking, memory pre-allocation, and fingerprinting.

随着英特尔傲腾数据中心持久化内存模块(DCPMM)开始进入市场以及远程直接内存访问(RDMA)硬件成本的降低,设计融合非易失性内存(NVM)和RDMA的键值(KV)数据库面临新的机遇和挑战.构建基于NVM和RDMA的KV数据库的关键在于设计一个高效的通信协议.遗憾的是,现有工作或采用NVM不感知的RDMA协议,或采用低效的NVM感知的RDMA协议,这导致它们无法最大化KV数据库的性能.本文提出了BOOM协议——一种新型的NVM感知的RDMA协议.相较于NVM不感知的协议,BOOM协议允许直接对远端NVM进行RDMA操作,消除了冗余的数据拷贝;相较于现有的NVM感知的协议,它可以显著减少元数据请求,降低KV请求的端对端延迟.在BOOM协议的基础上构建了BOOM-KV,并针对服务端中央处理器(CPU)利用率和宕机持久化等问题进一步进行优化.将BOOM-KV与最新的研究成果进行对比,结果表明,BOOM-KV能显著降低请求延迟,其中PUT延迟最大降低了42％,GET延迟最大降低了41％,并且展现出良好的扩展性.

A storage system is the core of a computer,and plays an important role in the sustainable develop-ment of emerging strategic industries,such as artifi-cial intelligence,big data,cloud computing,and the Internet of Things.Storage stack access is a major factor restricting the performance of data-intensive systems because of the increasing performance of processors and network devices.Recently,new storage devices have attracted wide attention due to their ability to break the"memory wall."These devices include block-addressable flash memory,byte-addressable non-volatile random access memory(NVRAM),in-memory computing devices,and large-capacity optical stor-age.Continuous innovation in algorithms,software designs,and hardware is necessary to build large-scale storage systems with high throughput,low ac-cess latency,and high data reliability.It can address challenges in building larger-scale and higher-perfor-mance systems with more complex structures.Fur-ther,it can boost the experience in building and ap-plying relevant systems and accelerate developing the big data processing.

Non-volatile memories(NVMs)provide lower latency and higher bandwidth than block devices.Besides,NVMs are byte-addressable and provide persistence that can be used as memory-level storage devices(non-volatile main memory,NVMM).These features change storage hierarchy and allow CPU to access persistent data using load/store in-structions.Thus,we can directly build a file system on NVMM.However,traditional file systems are designed based on slow block devices.They use a deep and complex software stack to optimize file system performance.This design results in software overhead being the dominant factor affecting NVMM file systems.Besides,scalability,crash consistency,data protection,and cross-media storage should be reconsidered in NVMM file systems.We survey existing work on optimizing NVMM file systems.First,we analyze the problems when directly using traditional file systems on NVMM,including heavy software overhead,limited scalability,inappropriate consistency guarantee techniques,etc.Second,we summarize the technique of 30 typical NVMM file systems and analyze their advantages and disadvantages.Finally,we provide a few suggestions for designing a high-performance NVMM file system based on real hardware Optane DC persistent memory module.Specifically,we suggest applying various techniques to reduce software overheads,improving the scalability of vir-tual file system(VFS),adopting highly-concurrent data structures(e.g.,lock and index),using memory protection keys(MPK)for data protection,and carefully designing data placement/migration for cross-media file system.

The work performance of researchers has a direct impact on biosafety, and effective personnel management is therefore important in high-level biosafety laboratories. Wuhan National Biosafety Laboratory has developed and implemented a biosafety scoring system (BSS) to reinforce its biosafety management system (BMS). Since the inception of the BSS, strict compliance with laboratory biosafety management regulations had been fostered among the research personnel. At the same time, the BSS has also effectively facilitated training work, reduced difficulties of personnel management, and strengthened operation and management of the laboratory, thereby played a positive role in ensuring biosafety of the laboratory. In conclusion, the biosafety scoring system exemplifies an effective personnel management of high-level biosafety laboratories.

Distributed persistent key-value store (KVS) plays an important role in today's storage infrastructure. The development of persistent memory (PM) and remote direct memory access (RDMA) allows to build distributed persistent KVS to provide fast data access. However, prior works focus on either PM-oriented or RDMA-oriented optimizations for key-value stores. We find these optimizations disallow a simple porting of RDMA-enabled KVS to PM or vice versa. This paper proposes FastStore, a high-performance distributed persistent KVS, by fully exploiting RDMA features and PM-friendly optimizations. First, FastStore utilizes RDMA-enabled PM exposure to establish direct indexing at the client side to reduce RTTs for reading values. Meanwhile, PM exposure allows PM sharing among cluster nodes, which helps to mitigate attribute-value skewness. Then, FastStore designs PM-friendly ownership transferring log and failure-atomic slotted-page allocator to achieve highly efficient PM management without PM leakage. Finally, FastStore proposes volatile search key to its B+tree indexing to reduce excessive PM accesses. We implement FastStore and the evaluation shows that FastStore outperforms the state-of-the-art ordered KVS Sherman by 2.8× higher throughput and 71.5% fewer RTTs.

闪存固态盘(SSD)具有高并行性、复杂的内部事务以及具有计算能力等特性.现有的存储系统是针对磁盘设计的,无法充分利用闪存固态盘的性能.本文介绍闪存固态盘给现有存储系统带来的问题与挑战,并从提高系统总带宽、减少固态盘内部事务对性能的影响、性能服务质量保证与利用固态盘的计算能力4方面阐述其性能优化关键技术,最后讨论了基于闪存固态盘的存储系统的发展方向.本文归纳指出,软硬件协同的设计方式是优化基于闪存固态盘的存储系统的发展趋势,软硬件协同设计有助于提供可预测的性能及可控的端到端延迟.

RDMA (Remote Direct Memory Access) is widely studied in building key-value stores to achieve ultra-low latency. In RDMA-based key-value stores, the indexing time takes a large fraction of the overall operation latency as RDMA enables fast data access. However, the single index structure used in existing RDMA-based key-value stores, either hash-based or sorted index, fails to support range queries efficiently while achieving high performance for singlepoint operations. In this paper, we explore the adoption of a hybrid index in the key-value stores based on RDMA, especially under the memory disaggregation architecture, to combine the benefits of a hash table and a sorted index. We propose HStore, an RDMA-based key-value store that uses a hash table for single-point lookups and leverages a skiplist for range queries to index the values stored in the memory pool. Guided by previous work on using RDMA for key-value services, HStore dedicatedly chooses different RDMA verbs to optimize the read and write performance. To efficiently keep the index structures within a hybrid index consistent, HStore asynchronously applies the updates to the sorted index by shipping the update log via two-sided verbs. Compared to state-of-the-art Sherman and Clover, HStore improves the throughput by up to 54.5% and 38.5% respectively under the YCSB benchmark.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), as the pathogen of coronavirus disease 2019 (COVID-19), has infected millions of people and took hundreds of thousands of lives. Unfortunately, there is deficiency of effective medicines to prevent or treat COVID-19. 3C like protease (3CL(Pro)) of SARS-CoV-2 is essential to the viral replication and transcription, and is an attractive target to develop anti-SARS-CoV-2 agents. Targeting on the 3CLPro, we screened our protease inhibitor library and obtained compound 10a as hit to weakly inhibit the SARS-CoV-2 3CL(Pro), and determined the co-crystal structure of 10a and the protease. Based on the deep understanding on the protein-ligand complexes between the hit and SARS-CoV-2 3CL(Pro), we designed a series of peptidomimetic inhibitors, with outstanding inhibitory activity against SARS-CoV-2 3CL(Pro) and excellent anti-viral potency against SARS-CoV-2. The protein-ligand complexes of the other key inhibitors with SARSCoV-2 3CL(Pro) were explicitly described by the X-ray co-crystal study. All such results suggest these peptidomimetic inhibitors could be further applied as encouraging drug candidates.

对于分布式存储系统来说,保证多租户尾延迟服务质量目标(SLO)同时获得较高的资源利用率十分重要.现有租户负载建模方法忽略了突发流量的密集程度,采用间接方法来预测尾延迟,导致系统的资源利用率较低.为了解决上述问题,本文基于密度聚类算法(DBScan),从强度、概率及密集程度3个维度对租户负载突发流量进行建模,直接预测连续突发流量期间请求延迟超限的概率.结合固定速率分配方法,本文设计了尾延迟SLO保证框架AccGecko.相比于已有的工作,AccGecko可以使系统平均多承载66％的租户.

RDMA (Remote Direct Memory Access) is widely exploited in building key-value stores to achieve ultra low latency. In RDMA-based key-value stores, the indexing time takes a large fraction (up to 74%) of the overall operation latency as RDMA enables fast data accesses. However, the single index structure used in existing RDMA-based key-value stores, either hash-based or sorted index, fails to support range queries efficiently while achieving high performance for single-point operations. In this paper, we reconsider the adoption of hybrid index in the key-value stores based on RDMA, to combine the benefits of hash table and sorted index. We propose HiStore, an RDMA-based key-value store using hash table for single-point lookups and leveraging skiplist for range queries. To maintain strong consistency in a lightweight and efficient approach, HiStore introduces index groups where a skiplist corresponds to a hash table, and asynchronously applies updates to the skiplist within a group. Guided by previous work on using RDMA for key-value services, HiStore dedicatedly chooses different RDMA primitives to optimize the read and write performance. Furthermore, HiStore tolerates the failures of servers that maintain index structures with index replication for high availability. Our evaluation results demonstrate that HiStore improves the performance of both GET and SCAN operations (by up to 2.03x) with hybrid index.

Distributed storage systems consolidate latency-critical (LC) tenants and best-effort (BE) tenants together to increase resources utilization. As a result, storage server processes require to guarantee differentiated tail latency SLOs for LC tenants, and meanwhile provide sustainable high bandwidth for BE tenants. With the adoption of high-performance NVMe SSDs, queuing time on storage server process contributes a large part to tail latency of LC tenant. Partitioning request queues, worker threads, and CPU cores on storage server processes among tenants helps to reduce interference and thus queuing time. This in turn requires careful core allocation to guarantee tail latency SLOs. In this paper, we argue that accurate core allocation is necessary for storage server processes to allocate the actual cores required by LC tenants. Thus, we propose QWin, a tail latency SLO-aware core allocation to enforce differentiated tail latency SLOs for multiple LC tenants. QWin first designs an SLO-to-core calculation model to accurately calculate the number of cores required by LC tenant. Bursty loads or fluctuated I/O latencies of storage devices can change core requirements of LC tenants. Thus, we design three core policies in QWin to adapt to the changing core requirements. We evaluate QWin by consolidating multiple LC and BE tenants together. The experiment results show that QWin outperforms the-state-of-the-art approaches in guaranteeing differentiated tail latency SLOs for LC tenants and meanwhile increasing bandwidth for BE tenants by 4x~21x.

The global public health crisis and economic losses resulting from the current novel coronavirus disease (COVID-19) pandemic have been dire. The most used real-time reverse transcription polymerase chain reaction (RT-PCR) method needs expensive equipment, technical expertise, and a long turnaround time. Therefore, there is a need for a rapid, accurate, and alternative technique of diagnosis that is deployable at resource-poor settings like point-of-care. This study combines heat deactivation and a novel mechanical lysis method by bead beating for quick and simple sample preparation. Then, using an optimized reverse transcription loop-mediated isothermal amplification (RT-LAMP) assay to target genes encoding the open reading frame 8 (ORF8), spike and nucleocapsid proteins of the novel coronavirus, SARS-CoV-2. The test results can be read simultaneously in fluorometric and colorimetric readouts within 40 min from sample collection. We also calibrated a template transfer tool to simplify sample addition into LAMP reactions when pipetting skills are needed. Most importantly, validation of the direct RT-LAMP system based on multiplexing primers S1:ORF8 in a ratio (1:0.8) using 143 patients' nasopharyngeal swab samples showed a diagnostic performance of 99.30% accuracy, with 98.81% sensitivity and 100% selectivity, compared to commercial RT-PCR kits. Since our workflow does not rely on RNA extraction and purification, the time-to-result is two times faster than other workflows with FDA emergency use authorization. Considering all its strengths: speed, simplicity, accuracy and extraction-free, the system can be useful for optimal point-of-care testing of COVID-19.