OpenCL, a modern parallel heterogeneous system programming language, enables problems to be partitioned and executed on modern CPU and GPU hardware, this increases performance of such applications considerably. Since GPU's are optimized for floating point and vector operations and specialize in them, they outperform general purpose CPU's in this field greatly. This language greatly simplifies the creation of applications for such heterogeneous system since it's cross-platform, vendor independent and is embeddable , hence letting it be used in any other general purpose programming language via libraries. There is more and more tools being developed that are aimed at low level programmers and scientists or engineers alike, that are developing applications or libraries for CPU’s and GPU’s of today as well as other heterogeneous platforms. The tendency today is to increase the number of cores or CPU‘s in hopes of increasing performance, however the increasing difficulty of parallelizing applications for such systems and the even increasing overhead of communication and synchronization are limiting the potential performance. This means that there is a point at which increasing cores or CPU‘s will no longer increase applications performance, and even can diminish performance. Even though parallel programming and GPU‘s with stream computing capabilities have decreased the need for communication and synchronization (since only the final result needs to be committed to memory), however this still is a weak link in developing such applications.
When analyzing stock market data, it is common to encounter observations that differ from the overall pattern. It is known as the problem of robustness. Presence of outlying observations in different data sets may strongly influence the result of classical (mean and standard deviation based) analysis methods or models based on this data. The problem of outliers can be handled by using robust estimators, therefore making aberrations less influential or ignoring them completely. An example of applying such procedures for outlier elimination in stock trading system optimization process is presented.