Hadean Blog

Removing the boundaries from distributed computing
Posted on Dec 1, 2017 10:38:49 AM
Making Genomic Alignment Predictable At Scale

In 2003, the International Human Genome Sequencing Consortium completed the first whole genome sequencing of human DNA after years of worldwide collaboration and billions of dollars of investment. Many human genomes have since been sequenced, the analysis of which has lead to many important discoveries. The factors that contribute to disease and if they affect different populations in different ways can be discovered by determining the correlations between people’s lifestyle, family history, environment and genomic data. There is now widespread motivation to sequence the genomes of hundreds of thousands, even millions, of individuals to learn even more. The global market for genomics is expected to reach USD 23.88b by 2022 (up from $14.71b in 2017), growing at an estimated CAGR of 10.2% from 2017 to 2020 due to increased investment in diagnostic services.

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Wynne Evans by Wynne Evans
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Posted on Dec 1, 2017 10:11:50 AM
MMORPGs: It’s time to rethink game architectures

 Large scale multiplayer games are outgrowing the wider gaming market (MMORPG 7.7%CAGR vs Gaming 6.2% CAGR) so it's no wonder game studios and developers are racing to create next gen MMORPG's.  They're looking to create lifelike chaotic worlds that won’t disappoint players with invisible walls, loading zones, sharding worlds, and crashes at world events. Yet to achieve this kind of immersion and scale requires a dedicated, expert team to design, build and support the necessary infrastructure. Capacity planning, deployment automation, orchestration - these are exactly the things you don’t want to sink time into as a game developer - they constrain your imagination. With finite budgets, you are left making a compromise: either scale back the immersion or reduce the scale of the world.

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Patrick Gordon by Patrick Gordon
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Posted on Nov 27, 2017 11:30:58 AM
Scaling Monte Carlo Simulations on Apache Spark. Can we do better?

The concept of big-data is simple - run relatively simple algorithms where the data-sets are so large that many machines are needed to hold it. The implementation, however, is surprisingly complex. For example, consider a map operation that transforms a list of doubles to a list of tuples each containing 3 double-precision values. We’ve just increased the amount of data we have by a factor of 3x. If the original list was partitioned in such a way that ½ the memory on a worker machine was being used to hold it, it’s necessary to repartition the list in such a way that that ¾ of the elements held on that machine must be held elsewhere. If we were producing strings rather than fixed sized objects, the problem becomes even harder - we cannot predict the size of partitions in advance. 

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Francis Russell by Francis Russell
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