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System Design with Model of Computation
Published in Hoi-Jun Yoo, Kangmin Lee, Jun Kyoung Kim, Low-Power NoC for High-Performance SoC Design, 2018
Hoi-Jun Yoo, Kangmin Lee, Jun Kyoung Kim
Table 2.2 shows the read/write–blocking/nonblocking relationship. For the read operation, nonblocking is not feasible. Nonblocking read implies that the receiving actor can fetch the required data not ready at the sending actor. That is, read is always blocking. For the write operation, blocked write implies that the sending actor should wait for the receiving actor to be ready. Nonblocked write allows one to write and forget. That is, there is a buffer between the sender and the receiver.
A Case Study: Vision Control
Published in Ivan Cibrario Bertolotti, Gabriele Manduchi, Real-Time Embedded Systems, 2017
Ivan Cibrario Bertolotti, Gabriele Manduchi
Function read() returns the number of bytes actually read, which is not necessarily equal to the number of bytes passed as argument. In fact, it may happen that at the time the function is called, not all the required bytes are available, and the program has to manage this properly. So, it is necessary to make sure that when read() is called, a frame is available for readout. The usual technique in Linux to synchronize read operation on device is the usage of the select() function, which allows a program to monitor multiple device descriptors, waiting until one or more devices become “ready” for some class of I/O operation (e.g., input data available). A device is considered ready if it is possible to perform the corresponding I/O operation (e.g., read) without blocking. Observe that the usage of select is very useful when a program has to deal with several devices. In fact, since read() is blocking, that is, it suspends the execution of the calling program until some data are available, a program reading on multiple devices may suspend in a read() operation regardless the fact that some other device may have data ready to be read. The arguments passed to select() are The number of involved devices;The read device mask;The write device maskThe mask of devices to be monitored for exceptions;The wait timeout specification.
Storing, preprocessing and analyzing tweets: finding the suitable noSQL system
Published in International Journal of Computers and Applications, 2022
Souad Amghar, Safae Cherdal, Salma Mouline
There is a lot of analysis tools such as Hadoop [20], Apache Spark [21], and Apache storm [22]: Hadoop is a software framework that provides large scale distributed data analysis. Hadoop provides HDFS (Hadoop Distributed File System ) which is a master-slave architecture that stores data and executes read and write instructions. Nevertheless, in some applications, we need to use other database systems instead of, or with, HDFS [20].Apache Spark is a unified engine for distributed data processing. It provides API (Application Programing Interfaces) in many programing languages and also supports many tools including structured data processing (Spark SQL), machine learning (MLlib) and graph processing (GraphX) [23].Apache Storm is a stream processing system that can process unbounded streams of data very fast. Storm applications are called topologies. A Storm topology is a graph of tasks that process distributed streams of data [22].
Job failure prediction in Hadoop based on log file analysis
Published in International Journal of Computers and Applications, 2022
Ehsan Shirzad, Hamid Saadatfar
Another parameter that can affect the jobs status is the volume of input/output data. To study this parameter, we considered the read and write byte counters of the jobs and calculated the sum of the values of these counters. Figure 3 shows the status of the jobs in I/O data volume ranges. In byte range (from one byte to less than one kilobyte) and KB range (from one kilobyte to less than one megabyte), the success rate of the jobs is almost the same and near 100%. However, an increase in I/O data volume decreases the success rate of the jobs, generally; and in TB range (one terabyte of I/O volume and more), the failure rate of the jobs is greatly increased. Thus, I/O data volume is another useful feature that we derived from the log files.
A read-disturb-free and write-ability enhanced 9T SRAM with data-aware write operation
Published in International Journal of Electronics, 2022
Jiaxun Lv, Zilin Wang, Maohang Huang, Yajuan He
The write/read power is defined as the power consumption of SRAM during write/read operations. As shown in Tables 3, 7T and 8T cells have relatively smaller read power consumption due to its less number of switched control signals and bit-lines. DA-9T, TG-9T, and SPG-11T SRAM cells have larger read power consumption due to the VVSS structure, which needs to be discharged before read operation and results in extra power consumption. The proposed 9T cell has the moderate read power, because 1T read path has relatively larger read current which results in larger read power than that of 8T cell. At a 0.5 V supply voltage, the read power consumption of our proposed 9T is 1.21× compared with 8T SRAM cell.