Performance Of Solid State Drives Research Articles

A Granularity-Based Clustering Method for Reducing Write Amplification in Solid-State Drives

In recent years, solid-state drives (SSDs) that adopt NAND flash memory have been widely used as the main storage devices. In particular, NAND flash memory has a special feature of “out-of-place” updates to write the up-to-date data to a free page, and the corresponding old page will become invalid. When the number of free pages in SSDs is insufficient, garbage collection (GC) will be executed to reclaim the invalid pages in a block by erasing the block. Many studies have shown that a good hot/cold data separation (i.e., clustering) can greatly reduce the overhead of GC so as to improve the SSD performance. However, previous clustering methods usually use a static number of clusters or a fixed size of granularity (i.e., a fine-grained or a coarse-grained granularity), so they may not always perform well for different kinds of workloads. Therefore, we propose a granularity-based clustering method to adaptively adjust the size of granularity groups for an appropriate number of clusters at runtime according to the update distances of logical addresses. According to the experimental results, we can improve SSD performance by reducing the overhead of GC and decrease the write amplification. Furthermore, we can show that the proposed method can utilize a fine-grained granularity to retain the record accuracy of update distances and also utilize a coarse-grained granularity to reduce the space required to record update distances.

A Hybrid Scheme Combining Duplications and LDPC Decoding to Reduce NAND Flash

Abstract With the development of NAND flash, the storage density has become larger and larger, but the reliability has become lower and lower. To address this problem, stronger error correction codes, low-density parity-check (LDPC) codes, are widely used in flash memory. However, the direct use of LDPC codes for error correction has a great impact on the read and write performance of SSDs. Given this, this paper proposes a new mentality that combines replica and LDPC decoding methods to improve the read performance of solid state drives (SSDs). Considering the characteristics of the quantization level of LDPC soft-decision decoding, this paper proposes the IDSD (LDPC soft-decision decoding and duplication method) and IDHSD (a method combining duplication and LDPC hybrid decoding)schemes. These schemes are mainly for hot data. When reading the data, the IDSD scheme needs to first judge whether it is soft-decision decoding. Then, according to the number of quantization levels recorded, it needs to select whether to use duplication or soft-decision decoding to correct the error data. The IDHSD scheme does not need to judge the soft-decision decoding but directly uses the duplication after the hard-decision decoding fails. Both schemes reduce the average response time to a certain extent and optimize the read performance of the SSD. The experimental results show that compared with the Native Scheme, the average response delay time of the IDSD scheme decreases by 5–30% and the IDHSD scheme slashes by 5–38%. Compared with the Traditional Refresh Scheme, the average response delay time of the IDSD scheme cuts down by 1–30% and the IDHSD scheme lowers by 1–29%.

On-Demand Garbage Collection Algorithm with Prioritized Victim Blocks for SSDs

Because of their numerous benefits, solid-state drives (SSDs) are increasingly being used in a wide range of applications, including data centers, cloud computing, and high-performance computing. The growing demand for SSDs has led to a continuous improvement in their technology and a reduction in their cost, making them a more accessible storage solution for a wide range of users. Garbage collection (GC) is a process that reclaims wasted storage space in NAND flash memories, which are used as the memory devices for SSDs. However, the GC process can cause performance degradation and lifetime reduction. This paper proposes an efficient garbage collection (GC) scheme that minimizes overhead by invoking GC operations only when necessary. Each GC operation is executed in a specific order based on the expected storage gain and the execution cost, ensuring that the storage space requirement is met while minimizing the frequency of GC invocation. This approach not only reduces the overhead due to GC, but also improves the overall performance of SSDs, including the latency and write amplification factor (WAF) which is an important indicator of the longevity of SSDs.

A STUDY ON LINUX KERNEL MODIFICATION FOR PERFORMANCE IMPROVEMENT OF SSD WEAR-OUT-AWARE SYSTEM

This study aims to improve the solid-state drive (SSD) wear-out-aware system by modifying the virtual file system (VFS) layer of the Linux kernel. Recently, according to the usage of big data and the development of artificial intelligence, the public can access it from a general PC rather than a research center. They installed several large-capacity SSDs required for learning individually or in a redundant array of inexpensive disks (RAID) 0 format on a PC to store the desired large amount of data. When multiple SSDs are connected and used, if one SSD suffers from wear-out, the input/output performance of other SSDs is adjusted to the corresponding device. Therefore, it is essential to ensure that wear out occurs evenly across multiple SSDs to prevent performance deterioration of the entire system. In this paper, we propose a method of distributing I/O to each SSD by modifying VFS layer of the Linux kernel to improve performance when the SSDs with the same performance but different performance due to wear-out. In other words, VFS was changed to detect each SSD wear out and distribute I/O to SSDs according to the performance ratio in a user-transparent way. Through the experimental results, when using the modified Linux kernel proposed in the paper, we found that the performance improved by 52% in the read and write functions compared to the original Linux kernel. Additionally, compared to RAID 0, it was found that there was a performance improvement of 26% in the read function and 16.7% in the write function. Keywords: Linux Kernel, Solid-State Drive, Redundant Array of Inexpensive Disks, Virtual File System DOI： https://doi.org/10.35741/issn.0258-2724.58.1.40

Parallelism-Aware Channel Partition for Read/Write Interference Mitigation in Solid-State Drives

The advancement of multi-level cell technology that enables storing multiple bits in a single NAND flash memory cell has increased the density and affordability of solid-state drives (SSDs). However, increased latency asymmetry between read and write (R/W) intensifies the severity of R/W interference, so reads cannot be processed for a long time owing to the extended flash memory resource occupancy of writing. Existing flash translation layer (FTL)-level mitigation techniques can allocate flash memory resources in a balanced manner taking R/W interference into account; however, due to the inefficient utilization of parallel flash memory resources, the effect on performance enhancement is restrictive. From the perspectives of the predicted access pattern and available concurrency of flash memory resources, we propose a parallelism-aware channel partition (PACP) scheme that prevents SSD performance degradation caused by R/W interference. Moreover, an additional performance improvement is achieved by reallocating interference-vulnerable page using leveraged garbage collection (GC) migration. The evaluation results showed that compared with the existing solution, PACP reduced the average read latency by 11.6% and average write latency by 6.0%, with a negligible storage overhead.

WA-OPShare: Workload-Adaptive Over-Provisioning Space Allocation for Multi-Tenant SSDs

Sharing a flash-based solid-state drive (SSD) among multiple tenants has become a common practice to improve storage utilization and cost efficiency. Meanwhile, how to allocate limited storage resources, especially the over-provisioning space (OPS) resources, among competitive tenants has emerged as a critical problem. The OPS refers to additional user-invisible storage space, whose size influences garbage collection (GC) efficiency. Due to unawareness of workload characteristics of different tenants, prior studies on multitenant OPS allocation lead to suboptimal SSD performance. In this article, we propose a novel workload-adaptive OPS allocation scheme for multitenant SSDs, called WA-OPShare. It targets an OPS sharing scheme that dynamically allocates the OPS among tenants to improve overall SSD performance. Two models are developed to identify underutilized storage space and predict the OPS-induced performance benefit of each tenant, respectively. Guided by the models, WA-OPShare regularly releases the underutilized storage space and then reallocates it to the tenant who can benefit the most. Experimental results show that compared to the traditional Partition and Sharing schemes, WA-OPShare improves the performance by up to 40.3% and 31.2%, and reduces the write amplification by up to 37.0% and 17.5%, respectively.

Understanding and Exploiting the Full Potential of SSD Address Remapping

Duplicate writes are prevalent in storage systems, originating from data duplication, journaling, and data relocations, etc. As flash-based solid state drives (SSDs) have been widely deployed, duplicate writes can significantly degrade their performance and lifetime. Prior studies have proposed innovative approaches that exploit the address remapping utility inside an SSD to eliminate duplicate writes. However, remap operations modify the logical-to-physical (L2P) address mapping table while the physical-to-logical (P2L) mappings persisted on flash memory remain unchanged. Such inconsistency between L2P and P2L mappings may cause data corruption and has long been a major obstacle to utilize SSD address remapping. In this article, we propose a novel SSD design, called Remap-SSD-LH, that realizes the full potential of SSD address remapping. It provides a remap primitive, which allows the host software and SSD firmware to perform logical writes of duplicate data at almost zero cost. To ensure mapping consistency as well as fast mapping lookups, Remap-SSD-LH employs a local log scheme based on hybrid storage. A local log is maintained for each flash garbage collection unit to record relevant P2L mapping changes induced by remap operations. The logs are stored in small nonvolatile RAM (NVRAM), e.g., capacitor-protected DRAM, and can be destaged to flash memory if NVRAM is full. We verify Remap-SSD-LH on a software SSD emulator with three case studies: 1) intra-SSD deduplication; 2) SQLite journaling; and 3) F2FS cleaning. The experimental results show that Remap-SSD-LH can maximally and efficiently exploit address remapping to improve SSD performance and lifetime.

Thermal Performance of the Thin Heat Pipe for Cooling of Solid-State Drives

With the rapid development of information science and technology, the demand for computer data processing is increasing, resulting in the rapid growth of the demand for high-power and high-performance solid-state drives (SSDs). The stable operation of SSDs plays an important role in ensuring the reliable working conditions and appropriate temperature of information technology equipment, rack servers, and related facilities. However, SSDs usually have significant heat emissions, putting forward higher requirements for temperature and humidity control, and consequently the heat sink system for cooling is essential to maintain the proper working state of SSDs. In this paper, a new type of thin heat pipe (THP) heat sink is proposed, and the heat transfer performance and cooling effect are experimentally and numerically studied. The numerical results are compared with experimental results, which showed an error within 5%. Single and double heat pipes were investigated under different input powers (from 5 W to 50 W) and different placement angles between 0° and 90°. The heat transfer performance of the new heat sink is analyzed by the startup performance, the evaporator temperature, and the total thermal resistance. The results show that the new double THPs with a 90° angle have a great advantage in the heat transfer performance of SSDs. The research is of great significance for the design and optimization of the SSDs’ cooling system in practical applications.

HCFTL: A Locality-Aware Flash Translation Layer for Efficient Address Translation

In modern solid-state drives (SSDs), a flash translation layer (FTL) is the core engine performing the logical-to-physical address translations. However, the increasing capacity of SSDs requires a large DRAM space to hold the mapping table in a page-level FTL. Due to the limited size of built-in DRAM, existing FTL schemes selectively cache some active mapping entries in DRAM, while store the entire mapping table on flash. However, the low cache hit ratio introduced by the traditional mapping entry eviction policy degrades the access performance of SSDs. In this article, we propose a novel FTL, hot-clusterity FTL (HCFTL), to improve the hit ratio of cached mapping table (CMT) of SSDs with limited cache space. HCFTL clusters mapping entries, which are newly evicted from the cache, into dynamic translation pages (DTPs). Reading entries from DTPs will increase the CMT hit ratio due to spatial and temporal localities. Because the logical page numbers (LPNs) in a DTP are not consecutive, we introduce two different efficient index structures to speedup the lookup of mapping entries in DTPs. Furthermore, to make the DTP index highly accurate, we additionally allocate an auxiliary cache to buffer the mapping entries newly evicted from CMT, and produce dynamic pages with minimum difference between the maximal and the minimal LPNs of entries in those pages. Our experiments show that HCFTL can improve the CMT hit ratio by up to 41.1% and decrease the system response time by up to 33.3%, compared to baseline FTL schemes.

Automatic Stream Identification to Improve Flash Endurance in Data Centers

The demand for high performance I/O in Storage-as-a-Service (SaaS) is increasing day by day. To address this demand, NAND Flash-based Solid-state Drives (SSDs) are commonly used in data centers as cache- or top-tiers in the storage rack ascribe to their superior performance compared to traditional hard disk drives (HDDs). Meanwhile, with the capital expenditure of SSDs declining and the storage capacity of SSDs increasing, all-flash data centers are evolving to serve cloud services better than SSD-HDD hybrid data centers. During this transition, the biggest challenge is how to reduce the Write Amplification Factor (WAF) as well as to improve the endurance of SSD since this device has a limited program/erase cycles. A specified case is that storing data with different lifetimes (i.e., I/O streams with similar temporal fetching patterns such as reaccess frequency) in one single SSD can cause high WAF, reduce the endurance, and downgrade the performance of SSDs. Motivated by this, multi-stream SSDs have been developed to enable data with a different lifetime to be stored in different SSD regions. The logic behind this is to reduce the internal movement of data—when garbage collection is triggered, there are high chances of having data blocks with either all the pages being invalid or valid. However, the limitation of this technology is that the system needs to manually assign the same streamID to data with a similar lifetime. Unfortunately, when data arrives, it is not known how important this data is and how long this data will stay unmodified. Moreover, according to our observation, with different definitions of a lifetime (i.e., different calculation formulas based on selected features previously exhibited by data, such as sequentiality, and frequency), streamID identification may have varying impacts on the final WAF of multi-stream SSDs. Thus, in this article, we first develop a portable and adaptable framework to study the impacts of different workload features and their combinations on write amplification. We then propose a feature-based stream identification approach, which automatically co-relates the measurable workload attributes (such as I/O size, I/O rate, and so on.) with high-level workload features (such as frequency, sequentiality, and so on.) and determines a right combination of workload features for assigning streamIDs . Finally, we develop an adaptable stream assignment technique to assign streamID for changing workloads dynamically. Our evaluation results show that our automation approach of stream detection and separation can effectively reduce the WAF by using appropriate features for stream assignment with minimal implementation overhead.

Lifespan-based garbage collection to improve SSD's reliability and performance

Given the “out-of-place” nature of write mode, Solid State Drive (SSD) has to execute a garbage collection (GC) to reclaim the space of invalidated data and free flash blocks for new written data. However, the GC can incur the movements of valid data inside SSD, resulting in the problems such as write latency, write amplification, etc. The GC overheads of SSD depend not only on the current write pattern but also on how data have been already placed in SSD. If data can be classified by their lifetime when written to SSD, and the data with similar lifetime are written into the same flash block, we can select the block with the shortest expected lifespan as the victim block. Ideally, the victim block contains few valid data, implying there are few valid pages that need to be migrated during the GC process with greatly reduced overhead. In this paper, we propose and implement a GC method, called Lifespan-based GC, for SSDs based on the data lifetime, together with the I/O distribution and the dynamic flash memory allocation. The results show that for those applications with the data lifetime characteristics, the lifespan-based GC can effectively reduce the amount of valid pages migrated in GC process, thereby minimizing the write latency and write amplification caused by GC.

An Efficient Block Address Transformation Scheme in Block Layer for Flash-Based SSDs

Flash-based solid state drives (SSDs) are widely adopted in both industry and the academia since SSDs offer higher performance, lower latency, and lower power consumption compared with traditional hard disk drives (HDDs). Unfortunately, the performance of SSDs can be affected by I/O access patterns. For example, random I/O operation degrades the SSD performance compared with sequential I/O operation since the random I/O operation reduces the spatial locality and increases garbage collection (GC) overhead. To handle this issue, in this article, we propose an efficient block address transformation scheme in the block layer to improve both performance and portability. To do this, we first transform random access patterns to sequential access patterns by sequentializing the block addresses in the block layer. Second, we devise a mapping table for managing transformed block addresses. Third, we support correct read operations and transaction processing for updating the mapping table to avoid sacrificing the consistency. Finally, we provide a cleaning scheme to reclaim invalid data generated by the transformed sequential access. This scheme increases spatial locality, reduces GC overhead, and operates well on any file systems or devices. Experimental results show the proposed scheme improves performance by up to 2x, 1.86x, 2.15x, and 42.8% compared with existing scheme in the case of diverse file systems such as EXT4, XFS, BTRFS, and F2FS, respectively.

EddySuperblock: Improving NAND Flash Efficiency and Lifetime by Endurance-Driven Dynamic Superblock Management

Solid-state drives (SSDs) are widely used in current storage systems. SSD manufacturers usually aggregate physical blocks across planes into one superblock to improve SSD performance. However, due to the wear endurance variations among blocks and pages, the standard superblock scheme leads to lifetime and space waste of SSDs. Superblock granularity also causes higher and more uncertain garbage collection overhead. To address these problems, this work proposes endurance-driven dynamic superblock management called EddySuperblock to provide a longer lifetime and higher space utilization with a faster and deterministic response time, which designs a dynamic superblock organization strategy that links free strong blocks into one superblock according to the real-time wear index of each block and introduces page-level wear management to further unlock the lifetime potential of pages. To reduce the high garbage collection overhead caused by superblocks, a nonblocking parallel garbage collection policy is proposed, which can reclaim space and serve requests simultaneously with low overhead. We conduct a set of experiments on a real hardware experiment platform. Both the proposed EddySuperblock and representative superblock schemes have been implemented on the experiment platform. The experimental results show that our scheme greatly reduces the average system response time by 47.6% and prolongs the lifetime by 75.6% compared with the state-of-the-art superblock scheme, and the final-state flash utilization reaches 95.4%. In the end, this article presents the application status of our work in real-world scenarios.

Performance Modeling and Practical Use Cases for Black-Box SSDs

Modern servers are actively deploying Solid-State Drives (SSDs) thanks to their high throughput and low latency. However, current server architects cannot achieve the full performance potential of commodity SSDs, as SSDs are complex devices designed for specific goals (e.g., latency, throughput, endurance, cost) with their internal mechanisms undisclosed to users. In this article, we propose SSDcheck , a novel SSD performance model to extract various internal mechanisms and predict the latency of next access to commodity black-box SSDs. We identify key performance-critical features (e.g., garbage collection, write buffering) and find their parameters (i.e., size, threshold) from each SSD by using our novel diagnosis code snippets. Then, SSDcheck constructs a performance model for a target SSD and dynamically manages the model to predict the latency of the next access. In addition, SSDcheck extracts and provides other useful internal mechanisms (e.g., fetch unit in multi-queue SSDs, background tasks triggering idle-time interval) for the storage system to fully exploit SSDs. By using those useful features and the performance model, we propose multiple practical use cases. Our evaluations show that SSDcheck’s performance model is highly accurate, and proposed use cases achieve significant performance improvement in various scenarios.

Self-Adapting Channel Allocation for Multiple Tenants Sharing SSD Devices

Solid-state drives (SSDs) have been widely deployed in high-performance data center environments, where multiple tenants usually share the same hardware. However, traditional SSDs distribute the users&#x2019; incoming data uniformly across all SSD channels, which leads to numerous access conflicts. Meanwhile, SSDs that blindly allocate one or several channels to one tenant sacrifice device parallelism and capacity. When SSDs are shared by tenants with different access patterns, inappropriate channel allocation results in SSD performance degradation. In this article, we propose a self-adapting channel allocation mechanism, named SSDKeeper, for multiple tenants that share one SSD. SSDKeeper employs a machine learning-assisted algorithm to take full advantage of SSD parallelism while providing performance isolation. By collecting multitenant access patterns, SSDKeeper predicts an optimal channel allocation strategy for multiple tenants using the well-trained model. To further consume the blocks in different channels evenly, SSDKeeper equips with a novel channel swap scheme to prolong the SSD lifespan. Comparing with traditional SSDs, SSDKeeper reduces the overall latency of read and write by 12.6&#x0025; and the lifespan is prolonged up to <inline-formula> <tex-math notation="LaTeX">$3.7\times $ </tex-math></inline-formula>.

HDFTL: An On-Demand Flash Translation Layer Algorithm for Hybrid Solid State Drives

NAND flash-based solid-state drives (SSDs) have been widely used in consumer electronic products such as smartphones, digital video recorders, and laptops. The flash translation layer (FTL) is crucial to the performance of SSDs. In this article, an on-demand FTL, briefly as HDFTL, is proposed for hybrid SSDs built with SLC+MLC flash memory. HDFTL divides the SLC into data blocks and translation blocks and uses all MLC as data blocks. All mapping items are stored in the translation block of the SLC, and partial mapping items are loaded into the cached mapping table of the FTL as needed. The contributions of HDFTL are as follows: (1) Unify the address mapping method of SLC and MLC areas to simplify the FTL design of hybrid SSDs; (2) Split the cached mapping table into a hot-write mapping table and a normal mapping table and use time locality to solve hot-write identification problem in hybrid SSDs; (3) According to the relative wear rate of SLC and MLC areas, adaptively adjust the size of the hot-write mapping table and the normal mapping table. Experimental results show that the performance of HDFTL is better than that of the existing FTLs for hybrid SSDs.

Optimizing Key-Value Stores for Flash-Based SSDs via Key Reshaping

Key-Value store (KV store) is becoming widely popular in both academia and industry due to its fast performance and simplicity in data management. To improve the performance of KV stores, recent Serial Advanced Technology Attachment (SATA) and Non-Volatile Memory express (NVMe) Solid-State Drives (SSDs) have been widely adopted. In contrast to the existing Hard-Disk Drives (HDDs), SSDs have unique characteristics which must be carefully considered to exploit the full performance. For example, due to the erase before write constraint, the access pattern of workloads impacts the performance and endurance of SSDs. Thus, the performance of SSD with the sequential workload is higher than that with the random workload. In this paper, we propose a key reshaping technique to improve the performance of KV stores with high performance storage devices. By reshaping keys, our scheme allows KV stores to process the random insert requests into sequential insert requests, improving request processing and Input/Output (I/O) performance. Our experimental results show that the proposed scheme can improve the performance of KV store by up to 106% and 281% compared with the existing scheme, in the case of SATA and NVMe SSDs, respectively.

Layer-Aware Request Scheduling for 3D Flash-Based SSDs

Recent development on 3D flash memories largely promotes the wide application of Solid-State Drives (SSDs) by providing larger capacity from vertically-stacked layers. However, there exist speed variations across different layers because of manufacturing process variations or physical designs, which induces new challenges to fully explore the advantages of existing SSDs, e.g. the parallel structure. This paper investigates the effect of speed variation on the parallel performance of SSDs. To balance chips’ workloads, traditional method selects chips in a round-robin way. As chip queue time can be estimated by some main factors, the chip also can be chosen in a greedy way. However, because of the layer speed variation, queue time estimation model should be modified. This paper first establishes a new queue time estimation model with the awareness of the flash layer information. Then the model is used to estimate the chip queue time and to direct write requests into the chip with the least queue time. The key idea is to largely reduce queue time of each write, thus reducing the average SSD response time. Finally, this new request redirection method is evaluated on SSDsim with real world workloads. Experimental results show that our model can estimate the queue time more accurately and our new request redirection method can improve 8.3% of write queue time on average under the situation of 4 times speed variation among 16 layers.

Boosting the Performance of SSDs via Fully Exploiting the Plane Level Parallelism

Solid state drives (SSDs) are constructed with multiple level parallel organization, including channels, chips, dies, and planes. Among these parallel levels, plane level parallelism, which is the last level parallelism of SSDs, has the most strict restrictions. Only the same type of operations that access the same address in different planes can be processed in parallel. In order to maximize the access performance, several previous works have been proposed to exploit the plane level parallelism for host accesses and internal operations of SSDs. However, our preliminary studies show that the plane level parallelism is far from well utilized and should be further improved. The reason is that the strict restrictions of plane level parallelism are hard to be satisfied. In this article, a from plane to die parallel optimization framework is proposed to exploit the plane level parallelism through smartly satisfying the strict restrictions all the time. In order to achieve the objective, there are at least two challenges. First, due to that host access patterns are always complex, receiving multiple same-type requests to different planes at the same time is uncommon. Second, there are many internal activities, such as garbage collection (GC), which may destroy the restrictions. In order to solve above challenges, two schemes are proposed in the SSD controller: First, a die level write construction scheme is designed to make sure there are always $N$ N pages of data written by each write operation. Second, in a further step, a die level GC scheme is proposed to activate GC in the unit of all planes in the same die. Combing the die level write and die level GC, write accesses from both host write operations and GC induced valid page movements can be processed in parallel at all time. To further improve the performance of SSDs, host write operations blocked by GCs are suggested to be processed in parallel with GC induced valid page movements, bringing lesser waiting time cost of host write operations. As a result, the GC cost and average write latency can be significantly reduced. Experiment results show that the proposed framework is able to significantly improve the write performance without read performance impact.

DEPS: Exploiting a Dynamic Error Prechecking Scheme to Improve the Read Performance of SSD

3-D NAND flash memory is gradually being widely used in solid state drives (SSDs), leading to increasing storage capacity. However, the read performance of SSD is sacrificed for decoding operations which are executed to guarantee the data reliability. No matter whether the data have bit errors, they will be sent to error correcting code (ECC) engine to decode, introducing a high read delay of SSD. Error prechecking can help to avoid the redundant decoding operations for the error-free data, but it induces extra checking overhead to the error data. Motivated by this, we carry out comprehensive experiments to analyze the distribution of bit errors in 3-D NAND flash memory. The preliminary experimental results show that there are a large number of pages read without errors in the early lifetime of 3-D NAND flash memory. Based on the observations and analyses, we propose a model to estimate the error-free ratio, and utilize it to design a dynamic error prechecking scheme (DEPS) to bypass the decoding operation for the error-free data in 3-D NAND flash memory and improve the read performance of SSD. Furthermore, by dividing a large page into small subpages, DEPS releases more error-free data, which significantly improves the read performance of SSD. Evaluation results from real-world traces demonstrate that by implementing DEPS, the average read performance of SSD is enhanced by 35%-55% with 3-D MLC NAND flash memory.