Purpose The purpose of this paper is to reveal and describe the divergent viewpoints about cybersecurity within a purposefully selected group of people with a range of expertise in relation to computer security. Design/methodology/approach Q methodology [Q] uses empirical evidence to differentiate subjective views and, therefore, behaviors in relation to any topic. Q uses the strengths of qualitative and quantitative research methods to reveal and describe the multiple, divergent viewpoints that exist within a group where individuals sort statements into a grid to represent their views. Analyses group similar views (sorts). In this study, participants were selected from a range of types related to cybersecurity (experts, authorities and uninformed). Findings Four unique viewpoints emerged such that one represents cybersecurity best practices and the remaining three viewpoints represent poor cybersecurity behaviors (Naïve Cybersecurity Practitioners, Worried but not Vigilant and How is Cybersecurity a Big Problem) that indicate a need for educational interventions within both the public and private sectors. Practical implications Understanding the divergent views about cybersecurity is important within smaller groups including classrooms, technology-based college majors, a company, a set of IT professionals or other targeted groups where understanding cybersecurity viewpoints can reveal the need for training, changes in behavior and/or the potential for security breaches which reflect the human factors of cybersecurity. Originality/value A review of the literature revealed that only large, nation-wide surveys have been used to investigate views of cybersecurity. Yet, surveys are not useful in small groups, whereas Q is designed to investigate behavior through revealing subjectivity within smaller groups.

Ray tracing is a well known technique to generate life-like images. Unfortunately, ray tracing complex scenes can require large amounts of CPU time and memory storage. Distributed memory parallel computers with large memory capacities and high processing speeds are ideal candidates to perform ray tracing. However, the computational cost of rendering pixels and patterns of data access cannot be predicted until runtime. To parallelize such an application efficiently on distributed memory parallel computers, the issues of database distribution, dynamic data management and dynamic load balancing must be addressed. In this paper, we present a parallel implementation of a ray tracing algorithm on the Intel Delta parallel computer. In our database distribution, a small fraction of database is duplicated on each processor, while the remaining part is evenly distributed among groups of processors. In the system, there are multiple copies of the entire database in the memory of groups of processors. Dynamic data management is acheived by an ALRU cache scheme which can exploit image coherence to reduce data movements in ray tracing consecutive pixels. We balance load among processors by distributing subimages to processors in a global fashion based on previous workload requests. The success of our implementation depends crucially on a number of parameters which are experimentally evaluated. © 1997 John Wiley & Sons, Ltd.

In a sort-last polygon rendering system, the efficiency of image composition is very important for achieving fast rendering. In this paper, the implementation of a sort-last rendering system on a general purpose multicomputer system is described. A two-phase sort-last-full image composition scheme is described first, and then many variants of it are presented for 2D mesh message-passing multicomputers, such as the Intel Delta and Paragon. All the proposed schemes are analyzed and experimentally evaluated on Caltech's Intel Delta machine for our sort-last parallel polygon renderer. Experimental results show that sort-last-sparse strategies are better suited than sort-last-full schemes for software implementation on a general purpose multicomputer system. Further, interleaved composition regions perform better than coherent regions. In a large multicomputer system. Performance can be improved by carefully scheduling the tasks of rendering and communication. Using 512 processors to render our test scenes, the peak rendering rate achieved on a 282,144 triangle dataset is dose to 4.6 million triangles per second which is comparable to the speed of current state-of-the-art graphics workstations.

Ray tracing is one of the computer graphics techniques used to render high quality images. Unfortunately, ray tracing complex scenes can require large amounts of CPU time, making the technique impractical for everyday use. Parallel ray tracing algorithms could potentially be used to reduce the high computational cost. However, pixel computation times can vary significantly, and naive attempts at parallelization give poor speedups due to load imbalance between the processors. In this paper, we evaluate the performance of three load balancing schemes for ray tracing on parallel processors, and propose two new load balancing strategies. To evaluate the performance, we implement all these strategies on the 512 processor Intel Touchstone Delta at Caltech.

The authors describe a fully distributed, parallel algorithm for ray-tracing problem. Load balancing is achieved through the use of comb distribution to roughly assign the same amount of pixels to each processor first, and then dynamically redistribute excessive loads among processors to keep each processor busy. In this model, there is no need for a master node to be responsible for dynamic scheduling. When each node finishes its job, it just requests an extra job from one of its neighbors. The authors implement their algorithm on Intel Delta Touchstone machine with 2-D mesh network topology and provide simulation results. With their scheme, they can get good speedup and high efficiency without much communication overhead.>