Did you ever imagine that there would be such an adverb in front of the term ‘parallel
computations’? Surprising right!! (no offence, I would say it a little weird too) Let’s dig
what’s more is coming out of it.
This topic is named after a workload problem that arises in the parallel job’s computation
environment in an acceptable way. It is an alternative used to say that it is embarrassingly
easy to do. Any parallel algorithm is called embarrassing when it can execute all by itself
without any dependency on peers nor communication from any external source. It even
needs less intervention while breaking a task into sub-components to make it execute like a parallel problem. This type of parallel paradigm helps in multiple executions of other jobs
and creating a job that is more professional made by itself. The efficiency resulted from
these tasks are more reliable and they are highly suitable for distributed computing
ambience. Thus, environments are actually present in supercomputer clusters with the least infrastructural designs and support the embarrassingly parallel algorithms.
The standard type of these algorithms can be expressed with the following rules:
- To achieve this name, the foremost quality of these algorithms is that the systematic
detailing of the computation steps is pre-defined in this model.
- The entire system consists of sub-modules and sub-tasks. So, each of these
components have to stored uniquely in different memory space.
- Independency of the algorithm can be obtained by provision of a clear routing path for
- To avoid intermediate conversations or communications between the submodules,
the initial and end nodes used in the computation are responsible for doing this job at
necessary intervals or pre-fixed periods.
Unlike the minimal outcomes out of these parallel algorithms, it has more of a bright side
for the complex connected distributed platforms. Another great advantage with these types
of algorithms is that they help to retribute the most common problems like parallel slowdown and parallel overhead in a well-maintained subtle background. In recent times, it is advanced to act dynamically by configuring a master and slave components in the parallel computations.
The most common application of the embarrassingly parallel computations is the rendering
of a 3D image using the ray tracing algorithm. This process is handled by the graphic processing unit (GPU) that is specially designed to support the high computation speed with the least level of resources embedded in it. As there are multiple sub-components inside this pixel ling and printing job, the calculations involve a series of mathematical derivations and geometrical operations for moving the variables. As all these guidelines are specified and trained ahead in the computation process, they switch between the operations like shifting, scaling, rotation and clipping of the associated variables.
Other non-prominent applications also include protein folding and unfolding software,
password cracking and some significant calculations like Mandelbrot set and Monte Carlo
The upgraded version of the Monte Carlo was proposed as Markov Chain Monte Carlo
(MCMC) algorithm. It looks like it is an advanced model of algorithm that can produce the
asymptotically correct samplings and practically proved the speed up the achievement in
burn-in processes. It is also proposed that this model can be extended on multiple machines within minimal interconnected systems. This type of system is encouraged as a MapReduce setting, which is supported by multi-level organizations to boost their processing and performance levels in the computations field.
In another research work, it has been proved that the embarrassingly parallel search
techniques are simple and reliable to solve the constraint programming models in the field
of artificial intelligence. It is seen that main processes in constraint programming involves
the data propagation process followed by the search process. It constitutes two level
instances like the distributed followed by the parallelized one. If a variable flow consistently
between these two levels made up by the CP solver, then it is said to be consistent variable.
This approach knowingly or unknowingly solves the classical problems that have a higher
capacity rates at the multi-core servers and CPU’s. This not only helps to parallelize the
resolution of a problem but also benefits to modify the solver code (rewrite any parallel
source code) and also replay the resolution of the given problem.
These are some of the least known, interesting facts and under-appreciated applications of
the embarrassingly parallel computations.