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Problems that we use computers to solve can be divided into three basic classes:

  • easy to solve
  • hard to solve
  • impossible to solve

Pretty much all the algorithms we use day-to-day fall into the easy class. Text editing, web browsing, searching, even problems such as weather prediction, which run on supercomputers, are examples of easy to solve problems. These problems are solvable with algorithms that are called polynomial-time (P). This means that, given a problem size of N, an algorithm exists to solve the problem in time that is proportional to a fixed power of N, written as O(N^k). For example, sorting a list of N words is solvable in O(N^2) time.

The class of hard-to-solve problems pretty much includes all problems involved in the creation and verification of a design. Synthesis, place-and-route, formal verification, all fall in this class. You  probably have heard the term NP-complete. NP-complete is just one class of many in the hard-to-solve category. The class of NP-complete problems is the easiest of the hard classes, which means that, often, these types of problems can be solved reasonably well. The hardest of the hard-to-solve classes that you are likely to encounter  is the PSPACE-complete class. Problems in this class are, for all intents of purposes, intractable. We will look at these two classes, plus another one that occurs frequently in design problems, the NP-hard class.

Both the easy and hard-to-solve classes are at least theoretically possible to solve. The last group, the impossible-to-solve group, consist of the set of uncomputable problems. These problems mostly are of theoretical interest, which makes sense, since nobody is going to make any money trying to solve problems that cannot be solved.

One note about complexity classes: I refer to the complexity of problem classes, not algorithms. For example, sorting is a problem, shell sort is an algorithm that solves the sorting problem.  So, saying sorting is an easy problem means that there exists some algorithm that solves it easily. Saying a problem is hard means there is no algorithm that solves it easily.

Since all design and verification related problems fall into the hard-to-solve category, this is what I will talk about most. However, the boundary between easy-to-solve and hard-to-solve problems gets very blurry when we start to look at problems in the real world.

But, before talking about this, let’s first look at a basic property of these different classes – scaling. Suppose we had an algorithm that computed a result for a problem instance of size N (say, sorting a list of size N) in time T. Now suppose we had a processor that ran ten times faster. How much larger problem size could our algorithm handle in the same amount of time T? The following table shows how N grows for a CPU performance increase of 10X for different complexities:

  • linear O(N) N -> 10N
  • O(N lgN) N ->  5.7N
  • O(N^2) N -> 3.2N
  • O(2^N) N -> N+3.4

What this shows is that capacity gains from increased processor performance  diminish with increased problem complexity. For algorithms that have exponential complexity, there is basically no significant gain in capacity from increased processor speed. For example, suppose I had an exponential verification algorithm that could handle 100 variables. Having a 10X faster processor would allow me to process 103 variables in the same amount of time. Considering that Moore’s law implies that the number of variables doubles every year or so, this does not bode well for our ability to keep up.

But, the fact is that verification does  manage to keep up with increased design sizes, albeit with difficulty. So, verification is not always an exponential problem. We will explore the reasons for this in the next post.

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