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+[[!meta title="Bioinformatic Supercomputer Wishlist"]]
+
+Many bioinformatic problems require large amounts of memory and
+processor time to complete. For example, running WGCNA across 10^6 CpG
+sites requires 10^6 choose 2 or 10^13 comparisons, which needs 10 TB
+to store the resulting matrix. While embarrassingly parallel, the
+dataset upon which the regressions are calculated is very large, and
+cannot fit into main memory of most existing supercomputers, which are
+often tuned for small-data fast-interconnect problems.
+
+Another problem which I am interested in is computing ancestral trees
+from whole human genomes. This involves running maximum likelihood
+calculations across 10^9 bases and thousands of samples. The matrix
+itself could potentially take 1 TB, and calculating the likelihood
+across that many positions is computationally expensive. Furthermore,
+an exhaustive search of trees for 2000 individuals requires 2000!!
+comparisons, or 10^2868; even searching a small fraction of that
+subspace requires lots of computational time.
+
+Some things that a future supercomputer could have that would enable
+better solutions to bioinformatic problems include:
+
+1. Fast local storage
+2. Better hierarchical storage with smarter caching. Data should
+   ideally move easily between local memory, shared memory, local
+   storage, and remote storage.
+3. Fault-tolerant, storage affinity aware schedulers. 
+4. GPUs and/or other coprocessors with larger memory and faster memory
+   interconnects.
+5. Larger memory (at least on some nodes)
+6. Support for docker (or similar) images. 
+7. Better bioinformatics software which can actually take advantage of
+   advances in computer architecture.
+
+[[!tag biology bioinformatics bluewaters]]