Survey of storage systems for high-performance computing

In current supercomputers, storage is typically provided by parallel distributed file systems for hot data and tape archives for cold data. These file systems are often compatible with local file systems due to their use of the POSIX interface and semantics, which eases development and debugging because applications can easily run both on workstations and supercomputers. There is a wide variety of file systems to choose from, each tuned for different use cases and implementing different optimizations. However, the overall application performance is often held back by I/O bottlenecks due to insufficient performance of file systems or I/O libraries for highly parallel workloads. Performance problems are dealt with using novel storage hardware technologies as well as alternative I/O semantics and interfaces. These approaches have to be integrated into the storage stack seamlessly to make them convenient to use. Upcoming storage systems abandon the traditional POSIX interface and semantics in favor of alternative concepts such as object and key-value storage; moreover, they heavily rely on technologies such as NVM and burst buffers to improve performance. Additional tiers of storage hardware will increase the importance of hierarchical storage management. Many of these changes will be disruptive and require application developers to rethink their approaches to data management and I/O. A thorough understanding of today's storage infrastructures, including their strengths and weaknesses, is crucially important for designing and implementing scalable storage systems suitable for demands of exascale computing

Towards Design and Analysis For High-Performance and Reliable SSDs

NAND Flash-based Solid State Disks have many attractive technical merits, such as low power consumption, light weight, shock resistance, sustainability of hotter operation regimes, and extraordinarily high performance for random read access, which makes SSDs immensely popular and be widely employed in different types of environments including portable devices, personal computers, large data centers, and distributed data systems. However, current SSDs still suffer from several critical inherent limitations, such as the inability of in-place-update, asymmetric read and write performance, slow garbage collection processes, limited endurance, and degraded write performance with the adoption of MLC and TLC techniques. To alleviate these limitations, we propose optimizations from both specific outside applications layer and SSDs\u27 internal layer. Since SSDs are good compromise between the performance and price, so SSDs are widely deployed as second layer caches sitting between DRAMs and hard disks to boost the system performance. Due to the special properties of SSDs such as the internal garbage collection processes and limited lifetime, traditional cache devices like DRAM and SRAM based optimizations might not work consistently for SSD-based cache. Therefore, for the outside applications layer, our work focus on integrating the special properties of SSDs into the optimizations of SSD caches. Moreover, our work also involves the alleviation of the increased Flash write latency and ECC complexity due to the adoption of MLC and TLC technologies by analyzing the real work workloads

Write Pattern Format Algorithm for Reliable NAND-Based SSDs

This brief presents and evaluates a pre-coding algorithm to reduce power consumption and improve data retention in NAND-based solid-state drives. Compared to the state-of-the-art asymmetric coding and stripe pattern elimination algorithm, the proposed write pattern format algorithm (WPFA) achieves better data retention while consuming less power. The hardware for WPFA is simpler and requires less circuitry. The performance of WPFA is evaluated by both computer simulations and field-programmable gate array implementation

Flash Memory Devices

Flash memory devices have represented a breakthrough in storage since their inception in the mid-1980s, and innovation is still ongoing. The peculiarity of such technology is an inherent flexibility in terms of performance and integration density according to the architecture devised for integration. The NOR Flash technology is still the workhorse of many code storage applications in the embedded world, ranging from microcontrollers for automotive environment to IoT smart devices. Their usage is also forecasted to be fundamental in emerging AI edge scenario. On the contrary, when massive data storage is required, NAND Flash memories are necessary to have in a system. You can find NAND Flash in USB sticks, cards, but most of all in Solid-State Drives (SSDs). Since SSDs are extremely demanding in terms of storage capacity, they fueled a new wave of innovation, namely the 3D architecture. Today “3D” means that multiple layers of memory cells are manufactured within the same piece of silicon, easily reaching a terabit capacity. So far, Flash architectures have always been based on "floating gate," where the information is stored by injecting electrons in a piece of polysilicon surrounded by oxide. On the contrary, emerging concepts are based on "charge trap" cells. In summary, flash memory devices represent the largest landscape of storage devices, and we expect more advancements in the coming years. This will require a lot of innovation in process technology, materials, circuit design, flash management algorithms, Error Correction Code and, finally, system co-design for new applications such as AI and security enforcement