  One of the hardest challenges of the current Big Data landscape is the lack
of ability to process huge volumes of information in an acceptable time. The
goal of this work, is to ascertain if it is useful to use typical Big Data
tools to solve High Performance Computing problems, by exploring and comparing
a distributed computing framework implemented on a commodity cluster
architecture: the experiment will depend on the computational time required
using tools such as Apache Spark. This will be compared to "equivalent more
traditional" approaches such as using a distributed memory model with MPI on a
distributed file system such as HDFS (Hadoop Distributed File System) and
native C libraries that create an interface to encapsulate this file system
functionalities, and using the GPI-2 implementation for the GASPI protocol and
it's in-memory checkpointing library to provide an application with Fault
Tolerance features. To be more precise, we've chosen the K-means algorithm as
experiment, that will be ran on variable size datasets, and then we will
compare the computational run time and time resilience of both approaches.
