Your summary is almost right but it does not reflect the essence of what mesos represents. The vision of mesosphere, the Company behind the project, is to create a “Datacenter Operating System” and the mesos is the kernel of it in analogy to the kernel of a normal OS. The API is not limited to … Read more
After two dozens of comments to understand the situation, it was found that the libhdf5.so.7 was actually a symlink (with several levels of indirection) to a file that was not shared between the queued processes and the interactive processes. This means even though the symlink itself lies on a shared filesystem, the contents of the … Read more
Replace your singleton cache with a distributed cache. One such cache could be JBoss Infinispan but I’m sure that other distributed cache and grid technologies exist, including commercial ones which are probably more mature at this point. For singleton objects in general, I’m not sure. I think I’d try to not have singletons in the … Read more
SKLearn manages its parallelism with Joblib. Joblib can swap out the multiprocessing backend for other distributed systems like dask.distributed or IPython Parallel. See this issue on the sklearn github page for details. Example using Joblib with Dask.distributed Code taken from the issue page linked above. from sklearn.externals.joblib import parallel_backend search = RandomizedSearchCV(model, param_space, cv=10, n_iter=1000, … Read more
In Spark 2.0+ version use spark session variable to set number of executors dynamically (from within program) spark.conf.set(“spark.executor.instances”, 4) spark.conf.set(“spark.executor.cores”, 4) In above case maximum 16 tasks will be executed at any given time. other option is dynamic allocation of executors as below – spark.conf.set(“spark.dynamicAllocation.enabled”, “true”) spark.conf.set(“spark.executor.cores”, 4) spark.conf.set(“spark.dynamicAllocation.minExecutors”,”1″) spark.conf.set(“spark.dynamicAllocation.maxExecutors”,”5″) This was you can let … Read more
The hierarchy of elements in Cassandra is: Cluster Data center(s) Rack(s) Server(s) Node (more accurately, a vnode) A Cluster is a collection of Data Centers. A Data Center is a collection of Racks. A Rack is a collection of Servers. A Server contains 256 virtual nodes (or vnodes) by default. A vnode is the data … Read more
From Software journal blog an extract. Clustering has a formal meaning. A cluster is a group of resources that are trying to achieve a common objective, and are aware of one another. Clustering usually involves setting up the resources (servers usually) to exchange details on a particular channel (port) and keep exchanging their states, so … Read more
MPI was deisgned tightly-coupled compute clusters with fast, reliable networks. Spread and ØMQ are designed for large distributed systems. If you’re designing a parallel scientific application, go with MPI, but if you are designing a persistent distributed system that needs to be resilient to faults and network instability, use one of the others. MPI has … Read more