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

A Study of Client-based Caching for Parallel I/O

The trend in parallel computing toward large-scale cluster computers running thousands of cooperating processes per application has led to an I/O bottleneck that has only gotten more severe as the the number of processing cores per CPU has increased. Current parallel file systems are able to provide high bandwidth file access for large contiguous file region accesses; however, applications repeatedly accessing small file regions on unaligned file region boundaries continue to experience poor I/O throughput due to the high overhead associated with accessing parallel file system data. In this dissertation we demonstrate how client-side file data caching can improve parallel file system throughput for applications performing frequent small and unaligned file I/O. We explore the impacts of cache page size and cache capacity using the popular FLASH I/O benchmark and explore a novel cache sharing approach that leverages the trend toward multi-core processors. We also explore a technique we call progressive page caching that represents cache data using dynamic data structures rather than fixed-size pages of file data. Finally, we explore a cache aggregation scheme that leverages the high-level file I/O interfaces provided by the PVFS file system to provide further performance enhancements. In summary, our results indicate that a correctly configured middleware-based file data cache can dramatically improve the performance of I/O workloads dominated by small unaligned file accesses. Further, we demonstrate that a well designed cache can offer stable performance even when the selected cache page granularity is not well matched to the provided workload. Finally, we have shown that high-level file system interfaces can significantly accelerate application performance, and interfaces beyond those currently envisioned by the MPI-IO standard could provide further performance benefits

Kilometer-scale climate models: Prospects and challenges

Currently major efforts are underway toward refining the horizontal resolution (or grid spacing) of climate models to about 1 km, using both global and regional climate models (GCMs and RCMs). Several groups have succeeded in conducting kilometer-scale multiweek GCM simulations and decadelong continental-scale RCM simulations. There is the well-founded hope that this increase in resolution represents a quantum jump in climate modeling, as it enables replacing the parameterization of moist convection by an explicit treatment. It is expected that this will improve the simulation of the water cycle and extreme events and reduce uncertainties in climate change projections. While kilometer-scale resolution is commonly employed in limited-area numerical weather prediction, enabling it on global scales for extended climate simulations requires a concerted effort. In this paper, we exploit an RCM that runs entirely on graphics processing units (GPUs) and show examples that highlight the prospects of this approach. A particular challenge addressed in this paper relates to the growth in output volumes. It is argued that the data avalanche of high-resolution simulations will make it impractical or impossible to store the data. Rather, repeating the simulation and conducting online analysis will become more efficient. A prototype of this methodology is presented. It makes use of a bit-reproducible model version that ensures reproducible simulations across hardware architectures, in conjunction with a data virtualization layer as a common interface for output analyses. An assessment of the potential of these novel approaches will be provided

Improving Storage Performance with Non-Volatile Memory-based Caching Systems

University of Minnesota Ph.D. dissertation. April 2017. Major: Computer Science. Advisor: David Du. 1 computer file (PDF); ix, 104 pages.With the rapid development of new types of non-volatile memory (NVRAM), e.g., 3D Xpoint, NVDIMM, and STT-MRAM, these technologies have been or will be integrated into current computer systems to work together with traditional DRAM. Compared with DRAM, which can cause data loss when the power fails or the system crashes, NVRAM's non-volatile nature makes it a better candidate as caching material. In the meantime, storage performance needs to keep up to process and accommodate the rapidly generated amounts of data around the world (a.k.a the big data problem). Throughout my Ph.D. research, I have been focusing on building novel NVRAM-based caching systems to provide cost-effective ways to improve storage system performance. To show the benefits of designing novel NVRAM-based caching systems, I target four representative storage devices and systems: solid state drives (SSDs), hard disk drives (HDDs), disk arrays, and high-performance computing (HPC) parallel file systems (PFSs). For SSDs, to mitigate their wear out problem and extend their lifespan, we propose two NVRAM-based buffer cache policies which can work together in different layers to maximally reduce SSD write traffic: a main memory buffer cache design named Hierarchical Adaptive Replacement Cache (H-ARC) and an internal SSD write buffer design named Write Traffic Reduction Buffer (WRB). H-ARC considers four factors (dirty, clean, recency, and frequency) to reduce write traffic and improve cache hit ratios in the host. WRB reduces block erasures and write traffic further inside an SSD by effectively exploiting temporal and spatial localities. For HDDs, to exploit their fast sequential access speed to improve I/O throughput, we propose a buffer cache policy, named I/O-Cache, that regroups and synchronizes long sets of consecutive dirty pages to take advantage of HDDs' fast sequential access speed and the non-volatile property of NVRAM. In addition, our new policy can dynamically separate the whole cache into a dirty cache and a clean cache, according to the characteristics of the workload, to decrease storage writes. For disk arrays, although numerous cache policies have been proposed, most are either targeted at main memory buffer caches or manage NVRAM as write buffers and separately manage DRAM as read caches. To the best of our knowledge, cooperative hybrid volatile and non-volatile memory buffer cache policies specifically designed for storage systems using newer NVRAM technologies have not been well studied. Based on our elaborate study of storage server block I/O traces, we propose a novel cooperative HybrId NVRAM and DRAM Buffer cACHe polIcy for storage arrays, named Hibachi. Hibachi treats read cache hits and write cache hits differently to maximize cache hit rates and judiciously adjusts the clean and the dirty cache sizes to capture workloads' tendencies. In addition, it converts random writes to sequential writes for high disk write throughput and further exploits storage server I/O workload characteristics to improve read performance. For modern complex HPC systems (e.g., supercomputers), data generated during checkpointing are bursty and so dominate HPC I/O traffic that relying solely on PFSs will slow down the whole HPC system. In order to increase HPC checkpointing speed, we propose an NVRAM-based burst buffer coordination system for PFSs, named collaborative distributed burst buffer (CDBB). Inspired by our observations of HPC application execution patterns and experimentations on HPC clusters, we design CDBB to coordinate all the available burst buffers, based on their priorities and states, to help overburdened burst buffers and maximize resource utilization

Remote sensing big data computing: challenges and opportunities

As we have entered an era of high resolution earth observation, the RS data are undergoing an explosive growth. The proliferation of data also give rise to the increasing complexity of RS data, like the diversity and higher dimensionality characteristic of the data. RS data are regarded as RS ‘‘Big Data’’. Fortunately, we are witness the coming technological leapfrogging. In this paper, we give a brief overview on the Big Data and data-intensive problems, including the analysis of RS Big Data, Big Data challenges, current techniques and works for processing RS Big Data

Proceedings of the Second International Workshop on Sustainable Ultrascale Computing Systems (NESUS 2015) Krakow, Poland

Proceedings of: Second International Workshop on Sustainable Ultrascale Computing Systems (NESUS 2015). Krakow (Poland), September 10-11, 2015

Helmholtz Portfolio Theme Large-Scale Data Management and Analysis (LSDMA)

The Helmholtz Association funded the "Large-Scale Data Management and Analysis" portfolio theme from 2012-2016. Four Helmholtz centres, six universities and another research institution in Germany joined to enable data-intensive science by optimising data life cycles in selected scientific communities. In our Data Life cycle Labs, data experts performed joint R&D together with scientific communities. The Data Services Integration Team focused on generic solutions applied by several communities