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نتیجه جستجو - حافظه دسترسی تصادفی

تعداد مقالات یافته شده: 4
ردیف عنوان نوع
1 RRAM serial configuration for the generation of random bits
پیکربندی سریال RRAM برای تولید بیت های تصادفی-2017
In this work the serial combination of two RRAM cells is studied for the generation of a random bit. Measure ments confirm that a serial reset operation, in which one of the two RRAMs switches to the high resistance state, is an unpredictable and random process. Furthermore, the same device switches during subsequent set and reset operations. This behavior paves the way for the application of this configuration for hardware security purposes.
Keywords: Resistive Random Access Memory (RRAM) | Random bit | Physical Unclonable Function (PUF) | Hardware security | Serial configuration
مقاله انگلیسی
2 Profiling Memory Vulnerability of Big-Data Applications
آشکار سازی آسیب پذیری حافظه از برنامه های داده های بزرگ-2016
Motivated by the increasing popularity of hosting in-memory big-data analytics in cloud, we present a profiling methodology that can understand how different memory subsystems, i.e., cache and memory bandwidth, are susceptible to the impact of interference from co-located applications. We first describe the design of the proposed tool and demonstrate a case study consisting of five Spark applications on real-life data set.
Keywords: Interference | Sparks | Bandwidth | Degradation | Sensitivity | Random access memory
مقاله انگلیسی
3 Bridging the I/O Performance Gap for Big Data Workloads: A New NVDIMM-based Approach
پل زدن به شکاف عملکرد I / O برای ظرفیت کارهای داده های بزرگ: رویکرد مبتنی بر NVDIMM جدید-2016
The long I/O latency posts significant challenges for many data-intensive applications, such as the emerging big data workloads. Recently, the NVDIMM (Non-Volatile Dual In-line Memory Module) technologies provide a promising solution to this problem. By employing non-volatile NAND flash memory as storage media and connecting them via DIMM (Dual Inline Memory Module) slots, the NVDIMM devices are exposed to memory bus so the access latencies due to going through I/O controllers can be significantly mitigated. However, placing NVDIMM on the memory bus introduces new challenges. For instance, by mixing I/O and memory traffic, NVDIMM can cause severe performance degradation on memory-intensive applications. Besides, there exists a speed mismatch between fast memory access and slow flash read/write operations. Moreover, garbage collection (GC) in NAND flash may cause up to several millisecond latency. This paper presents novel, enabling mechanisms that allow NVDIMM to more effectively bridge the I/O performance gap for big data workloads. To address the workload heterogeneity challenge, we develop a scheduling scheme in memory controller to minimize the interference between the native and the I/O-derived memory traffic by exploiting both data access criticality and resource utilization. For NVDIMM controller, several mechanisms are designed to better orchestrate traffic between the memory controller and NAND flash to alleviate the speed discrepancy issue. To mitigate the lengthy GC period, we propose a proactive GC scheme for the NVDIMM controller and flash controller to intelligently synchronize and transfer data involving in forthcoming GC operations. We present detailed evaluation and analysis to quantify how well our techniques fit with the NVDIMM design. Our experimental results show that overall the proposed techniques yield 10%∼35% performance improvements over the state-of-the-art baseline schemes.
Keywords: Nonvolatile memory | Random access memory | Throughput | Performance evaluation | Interference
مقاله انگلیسی
4 Big Data Analytics Integrating a Parallel Columnar DBMS and the R Language
یکپارچه سازی تحلیل داده های بزرگ یک DBMS ستونی موازی و زبان R-2016
Most research has proposed scalable and parallel analytic algorithms that work outside a DBMS. On the other hand, R has become a very popular system to perform machine learning analysis, but it is limited by main memory and singlethreaded processing. Recently, novel columnar DBMSs have shown to provide orders of magnitude improvement in SQL query processing speed, preserving the parallel speedup of rowbased parallel DBMSs. With that motivation in mind, we present COLUMNAR, a system integrating a parallel columnar DBMS and R, that can directly compute models on large data sets stored as relational tables. Our algorithms are based on a combination of SQL queries, user-defined functions (UDFs) and R calls, where SQL queries and UDFs compute data set summaries that are sent to R to compute models in RAM. Since our hybrid algorithms exploit the DBMS for the most demanding computations involving the data set, they show linear scalability and are highly parallel. Our algorithms generally require one pass on the data set or a few passes otherwise (i.e. fewer passes than traditional methods). Our system can analyze data sets faster than R even when they fit in RAM and it also eliminates memory limitations in R when data sets exceed RAM size. On the other hand, it is an order of magnitude faster than Spark (a prominent Hadoop system) and a traditional row-based DBMS.
Keywords: Mathematical model | Computational modeling | Random access memory | Data models | Load modeling | Layout | Numerical models
مقاله انگلیسی
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