I am trying to get into distributed computing so this article is particularly interesting to me. I may be mistaken so please excuse my naivety if my points are off marks. I thought MPI was mainly geared towards communication-heavy tasks where the underlying network is specialized, for example infiniband or bus between CPUs. One use of MPI is to manage distributed memory tasks between different physical CPUs while threads run on multiple cores of same CPU. Spark, I believe, doesn't handle cases like this well because JVM hides low level details. I have read papers that propose to layer MPI over RDMA rather than expose a flat memory model, which came as a surprise to me but it shows the flexibility of MPI. One thing unclear to me what performance we can expect from MPI when we use commodity network gears, and how it compares to Spark. The article is absolutely correct MPI leaves robustness to user and that is today an oversight.

Modern Hadoop ecosystem is designed for different workload from MPI's. It emphasizes co-localizing date and computation, seamless robustness,and trades off raw power for simple programmingmodels. MapReduce turns out too simple, so Spark implements graph execution, which is nothing new to HPC. As far I know Spark's authors don't believe it is ready for distributed numerical linear algebra yet. But a counterpoint is that I am seeing machine learning libraries using Spark, so perhaps things are improving.

One thing I have learnt today is that MPI isn't gaining popularity. I just have a hard time picturing a JVM language in overall control in HPC where precise control of memory is paramount to performance.