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ردیف | عنوان | نوع |
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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 |