Beautiful Code [39]
13.1. The User Interface of the Gene Sorter
The Gene Sorter can collect all the known genes in disease-related regions of DNA into a list of candidate genes. This list is displayed in a table, illustrated in Figure 13-1, that includes summary information on each gene and hyperlinks to additional information. The candidate list can be filtered to eliminate genes that are obviously not relevant, such as genes expressed only in the kidneys when the viewer is researching a genetic disease of the lungs. The Gene Sorter is also useful in other contexts where one wants to look at more than one gene at once, such as when one is studying genes that are expressed in similar ways or genes that have similar known functions. The Gene Sorter is available currently for the human, mouse, rat, fruit fly, and C. elegans genomes.
The controls on the top of the screen specify which version of which genome to use. The table underneath contains one row per gene.
Figure 13-1. Main page of the Gene Sorter
A separate configuration page controls which columns are displayed in the table and how they are displayed. A filter page can be used to filter out genes based on any combination of column values.
The table can be sorted a number of ways. In this case, it is sorted by proximity to the selected gene, SYP. SYP is a gene involved with the release of neurotransmitters.
How Elegant Code Evolves with Hardware The Case of Gaussian Elimination > The Effects of Computer Architectures on Matrix Algorithms
14. How Elegant Code Evolves with Hardware The Case of Gaussian Elimination
Jack Dongarra and Piotr Luszczek
The increasing availability of advanced-architecture computers, at affordable costs, has had a significant effect on all spheres of scientific computation. In this chapter, we'll show the need for designers of computing algorithms to make expeditious and substantial adaptations to algorithms, in reaction to architecture changes, by closely examining one simple but important algorithm in mathematical software: Gaussian elimination for the solution of linear systems of equations.
At the application level, science has to be captured in mathematical models, which in turn are expressed algorithmically and ultimately encoded as software. At the software level, there is a continuous tension between performance and portability on the one hand, and understandability of the underlying code. We'll examine these issues and look at trade-offs that have been made over time. Linear algebra—in particular, the solution of linear systems of equations—lies at the heart of most calculations in scientific computing. This chapter focuses on some of the recent developments in linear algebra software designed to exploit advanced-architecture computers over the decades.
There are two broad classes of algorithms: those for dense matrices and those for sparse matrices. A matrix is called sparse if it contains a substantial number of zero elements. For sparse matrices, radical savings in space and execution time can be achieved through specialized storage and algorithms. To narrow our discussion and keep it simple, we will look only at the dense matrix problem (a dense matrix is defined as one with few zero elements).
Much of the work in developing linear algebra software for advanced-architecture computers is motivated by the need to solve large problems on the fastest computers available. In this chapter, we'll discuss the development of standards for linear algebra software, the building blocks for software libraries, and aspects of algorithm design as influenced by the opportunities for parallel implementation. We'll explain motivations for this work, and say a bit about future directions.
As representative examples of dense matrix routines, we will consider Gaussian elimination, or LU factorization. This examination, spanning hardware and software advances over the past 30 years, will highlight the most important factors that must be considered in designing linear algebra software for advanced-architecture computers. We use these factorization