ECE1724 Project Final Report: HashLife on GPU

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7 ConclusionsMy inability to really implement <strong>HashLife</strong> on the GPU is disheartening, and the resulting speedup not much betterthan a direct translation of Life to the GPU. It was however a salvageable educational experience as I learnt a littlemore about the CUDA memory hierarchy and corrected my assumption that dispatching threads costs time as it doeson operating systems running on CPUs.In retrospect, I made one major mistake: I tackled both the CPU and GPU implementations of <strong>HashLife</strong> at thesame time. I would have been better off to implement a plain <strong>HashLife</strong> on the CPU with recursion and dynamicmemory, and then find out how to transform it to operate without them.In fact, I can now see that the solution is to collapse the quad-tree spatial representation into the temporary 9-treeused for computation. It may appear inefficient, but the overhead of five extra pointers per node would have beenrapidly absorbed as the grid size increased, and the static allocation would have benefited performance also. Anyremaining memory on the GPU would then be used for a large static hash table which would use linear probing toresolve collisions (e.g.: finds the next empty spot), and eject old entries in a Least-Recently-Used manner.Also, the recursion could have been converted into iteration with some help from the host CPU:1. Run the kernel on one thread at the root of the tree to check if it needs updating or if the next state is in the hashtable, return the status back to the CPU.2. Based on that status, run the kernel on up-to four threads on the next level down of the quad-tree ’subset’ andcheck if updating is needed.3. If part of the tree needs updating, run the kernel on nine threads on the ’temporary’ overlapping 9-tree childnodes used for computation. Return their status to the host CPU.4. Repeat until all ’quad-tree’ nodes are visited, and all 9-tree nodes are updated, then start over at the root node.Despite the overhead of repeatedly launching kernels and copying data back to the CPU, the algorithm would stillbehave as it should and try to reduce the computations to hash table lookups.6
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