The mean processing time per frame with the optimized implementation is 17.6 ms (we add 1 ms for the ARM/DSP data transfer) whereas the nonoptimized implementation has a processing time of 80.85 ms. The optimized processing time represents 22% of the nonoptimized one.
On a resource-constrained TMoteSky device [41] using a 8 MHz processor, the current packet conversion implementation requires a processing time of less than 1 ms. By delegating common tasks, such as buffer management, packet creation, QoS management and aggregation, IDRA network services require up to a factor 2-10 less memory than traditional network protocols.
The implementation has achieved a processing speed of about 26 frames per second at an operating frequency of 33.33 MHz.
In implementation, nextflu consists of a processing pipeline written in Python called augur that analyzes virus sequence data and a JavaScript-based browser visualization called auspice that displays this processed information.
MapReduce is a widely used programming model and an associated implementation for processing and generating big data sets with a parallel, distributed algorithm on a cluster [21, 22].
The Xilinx VirtexE BTC implementation has shown to provide a processing speed of about 1.113 × 106 of pixels per second with a compression ratio which varies between 1.25 and 2 bits/pixel, according to the image nature.
In our work, we are particularly interested in reducing the implementation complexity especially for such a processing that operates on a pixel basis.
More specifically, we chose to examine a field-programmable gate array (FPGA) hardware solution with a budget FPGA and high performance FPGA, a mixed hardware/software FPGA implementation, a general purpose graphics processing unit (GPGPU) implementation, and a strictly software solution in MATLAB.
1 https://issues.apache.org/jira/browse/HADOOP. 2 MapReduce is a programming model and an associated implementation for processing and generating large datasets that is amenable to a broad variety of real-world tasks [38].
MapReduce [2] is a parallel and distributed programming model and also an associated implementation for processing huge volumes of data on a large cluster of commodity machines.
