This ebook is the first authorized digital version of Kernighan and Ritchie's 1988 classic, The C Programming Language (2nd Ed.). One of the best-selling programming books published in the last fifty years, "K&R" has been called everything from the "bible" to "a landmark in computer science" and it has influenced generations of programmers. Available now for all leading ebook platforms, this concise and beautifully written text is a "must-have" reference for every serious programmers digital library.

As modestly described by the authors in the Preface to the First Edition, this "is not an introductory programming manual; it assumes some familiarity with basic programming concepts like variables, assignment statements, loops, and functions. Nonetheless, a novice programmer should be able to read along and pick up the language, although access to a more knowledgeable colleague will help."

The C programming language

We consider rule sets for internet packet routing and filtering, where each rule consists of a range of source addresses, a range of destination addresses, a priority, and an action. A given packet should be handled by the action from the maximum priority rule that matches its source and destination. We describe new data structures for quickly finding the rule matching an incoming packet, in near-linear space, and a new algorithm for determining whether a rule set contains any conflicts, in time O(n3/2).

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Internet packet filter management and rectangle geometry

We consider rule sets for internet packet routing and filtering, where each rule consists of a range of source addresses, a range of destination addresses, a priority, and an action. A given packet should be handled by the action from the maximum priority rule that matches its source and destination. We describe new data structures for quickly finding the rule matching an incoming packet, in near-linear space, and a new algorithm for determining whether a rule set contains any conflicts, in time O(n^{3/2}).

Internet Packet Filter Management and Rectangle Geometry

Consider a rooted tree whose nodes can be in two states: marked or unmarked. The marked ancestor problem is to maintain a data structure with the following operations: mark(v) marks node v: unmark(v) removes any marks from node v; firstmarked(v) returns the first marked node on the path from v to the root. We show tight upper and lower bounds for the marked ancestor problem. The lower bounds are proved in the cell probe model, the algorithms run on a unit-cost RAM. As easy corollaries we prove (often optimal) lower bounds on a number of problems. These include planar range searching, including the existential or emptiness problem, priority search trees static tree union-find, and several problems from dynamic computational geometry, including segment intersection, interval maintenance, and ray shooting in the plane. Our upper bounds improve algorithms from various fields, including coloured ancestor problems and maintenance of balanced parentheses.

Marked ancestor problems

Algorithms for Memory Hierarchies

Optimal on-line decremental connectivity in trees

The behaviour of three methods for constructing a binary heap is studied. The methods considered are the original one proposed by Williams [1964], in which elements are repeatedly inserted into a single heap; the improvement by Floyd [1964], in which small heaps are repeatedly merged to bigger heaps; and a recent method proposed, e. g., by Fadel et al. [1999] in which a heap is built layerwise. Both the worst-case number of instructions and that of cache misses are analysed. It is well-known that Floyd's method has the best instruction count. Let N denote the size of the heap to be constructed, B the number of elements that fit into a cache line, and let c and d be some positive constants. Our analysis shows that, under reasonable assumptions, repeated insertion and layerwise construction both incur at most cN/B cache misses, whereas repeated merging, as programmed by Floyd, can incur more than (dN log2 B)/B cache misses. However, for a memory-tuned version of repeated merging the number of cache misses incurred is close to the optimal bound N/B.

Performance Engineering Case Study: Heap Construction

The behaviour of three methods for constructing a binary heap on a computer with a hierarchical memory is studied. The methods considered are the original one proposed by Williams [1964], in which elements are repeatedly inserted into a single heap; the improvement by Floyd [1964], in which small heaps are repeatedly merged to bigger heaps; and a recent method proposed, e.g., by Fadel et al. [1999] in which a heap is built layerwise. Both the worst-case number of instructions and that of cache misses are analysed. It is well-known that Floyd's method has the best instruction count. Let N denote the size of the heap to be constructed, B the number of elements that fit into a cache line, and let c and d be some positive constants. Our analysis shows that, under reasonable assumptions, repeated insertion and layerwise construction both incur at most cN/B cache misses, whereas repeated merging, as programmed by Floyd, can incur more than (dN log2 B)/B cache misses. However, for our memory-tuned versions of repeated insertion and repeated merging the number of cache misses incurred is close to the optimal bound N/B. In addition to these theoretical findings, we communicate many practical experiences which we hope to be valuable for others doing experimental algorithmic work.

Maz Spork

Papers

Optimal on-line decremental connectivity in trees

Performance Engineering Case Study: Heap Construction

Performance engineering case study: heap construction