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    1. POSolving linear systems of equations
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    1. COWhat do you mean by solving the equations in non-sequential order? I know now that this problem is actually a multi variable optimizing problem (learning stuff as I go, this project has a habit of doing that to me). However its an optimizing problem within an optimizing problem, thus i can parallelize individual optimizing problems. If I get the main CPU to setup the problems, would CUDA be able to adjust parameters itself and do iterations without being explicitly fed data by the CPU? As opposed to CPU prepping a matrix and CUDA solving it and simply returning it, nothing else?
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    2. COThink of GPU as a multicore processor. GPU has many cores, but each core is much weaker than CPU core. GPU has its strength from parallelization. Can you split the whole problem into sub-tasks which can be executed interdependently? Say, you have equations 1, 2, ..., N. Can you solve them independently? If so, CUDA may help. You could try to parallelize your code on CPU first, because making the same with CUDA is harder. In my experience, linear algebra is notoriously hard to parallelize, unless of course, the problem consists of independent sub-tasks.
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    3. COCUDA is a programming language similar to C. GPU coding involves explicit memory management. You must be very careful about moving data around. Solving one 6x6 equation and returning the answer to CPU doesn't justify the overhead. In order to benefit from GPU, your algorithm must be able to streamline very large number of equations at once. There should be no dependency between streamlined equations. Then GPU can solve all these equation faster than CPU.
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