Our First Assembly Language Program

2021 ◽  
pp. 11-37
Author(s):  
Stephen Smith
Author(s):  
Nikita Igorevitch Nikiforov ◽  
Dmitry Yuryevitch Volkanov

This paper addresses the problem of packet classification within a network processor (NP) architecture without the separate associative device. By the classification, we mean the process of identifying a packet by its header. The classification stage requires the implementation of data structures to store the flow tables. In our work, we consider the NP without the associative memory. Flow tables are represented by an assembly language program in the NP. For translating flow tables into assembly language programs, a tables translator was used. The main reason for implementing data compression algorithms in a flow tables translator is that modern flow tables can take up to tens of megabytes. In this paper, we describe the following data compression algorithms: Optimal rule caching, recursive end-point cutting and common data compression algorithms. An evaluation of the implemented data compression algorithms was performed on a simulation model of the NP.


2017 ◽  
Vol 29 (5) ◽  
pp. 808-818
Author(s):  
Tomohiro Harada ◽  
Keiki Takadama ◽  
◽  

This study proposes a novel genetic programming method using asynchronous reference-based evaluation (called AREGP) to evolve computer programs through single-event upsets (SEUs) in the on-board computer in space missions. AREGP is an extension of Tierra-based asynchronous genetic programming (TAGP), which was proposed in our previous study. It is based on the idea of the biological simulator, Tierra, where digital creatures are evolved through bit inversions in a program. AREGP not only inherits the advantages of TAGP but also overcomes its limitation, i.e., TAGP cannot select good programs for evolution without an appropriate threshold. Specifically, AREGP introduces an archive mechanism to maintain good programs and a reference-based evaluation by using the archive for appropriate threshold selection and removal. To investigate the effectiveness of the proposed AREGP, simulation experiments are performed to evolve the assembly language program in the SEU environment. In these experiments, the PIC instruction set, which is carried on many types of spacecraft, is used as the evolved assembly program. The experimental results revealed that AREGP cannot only maintain the correct program through SEU with high occurrence rate, but is also better at reducing the size of programs in comparison with TAGP. Additionally, AREGP can achieve a shorter execution step and smaller size of programs, which cannot be achieved by TAGP.


Author(s):  
A. V. Crewe ◽  
M. Ohtsuki

We have assembled an image processing system for use with our high resolution STEM for the particular purpose of working with low dose images of biological specimens. The system is quite flexible, however, and can be used for a wide variety of images.The original images are stored on magnetic tape at the microscope using the digitized signals from the detectors. For low dose imaging, these are “first scan” exposures using an automatic montage system. One Nova minicomputer and one tape drive are dedicated to this task.The principal component of the image analysis system is a Lexidata 3400 frame store memory. This memory is arranged in a 640 x 512 x 16 bit configuration. Images are displayed simultaneously on two high resolution monitors, one color and one black and white. Interaction with the memory is obtained using a Nova 4 (32K) computer and a trackball and switch unit provided by Lexidata.The language used is BASIC and uses a variety of assembly language Calls, some provided by Lexidata, but the majority written by students (D. Kopf and N. Townes).


1977 ◽  
Vol 8 (4) ◽  
pp. 250-255 ◽  
Author(s):  
Hildegarde Traywick

This paper describes the organization and implementation of an effective speech and language program in the public schools of Madison County, Alabama, a rural, sparsely settled area.


2002 ◽  
Vol 3 (1) ◽  
pp. 12-14
Author(s):  
Joanne Toennies ◽  
Chris Bauman ◽  
Susan Huntenburg

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