CS + + : Reinventing Computer Science ( for Secondary Storage )
The challenges of developing data structures and language which operate at the level demanded by SS are described, and some benefits of abstracting over looping are looked at: enormous reductions in code for building large systems, intrinsic parallelization, and easy incorporation of the Internet.
Abstract
Secondary storage (”SS”) offers a significantly different memory organization from RAM, which most of computer science is geared to support. This forces a revision of data structures and algorithms on one hand and of programming language on the other. Algorithms become simpler, but in the frequent situation that the data structure sizes exceed RAM capacity, even polynomial complexity becomes intractable unless it is sub-quadratic. Languages are obliged to abstract over looping, which presents opportunities for programming at a much higher level than allowed by the “von Neumann bottleneck” that restricts most languages geared for RAM. This paper describes the challenges of developing data structures and language which operate at the level demanded by SS. We look at some benefits of abstracting over looping: enormous reductions in code for building large systems, intrinsic parallelization, and easy incorporation of the Internet. We note a diversity of SS applications, including expert systems, data mining, and semistructured data, as well as conventional organizational management. We will see that Computer Science SS also has benefits for ordinary RAM programming, and will touch on some data structure ideas as illustration.