Abstract
This paper presents a set of methods and techniques to remove inefficiencies in a data analysis application used in searches by the ATLAS Experiment at the Large Hadron Collider. Profiling scientific code helped to pinpoint design and runtime inefficiencies, the former due to coding and data structure design. The data analysis code used by groups doing searches in the ATLAS Experiment contributed to clearly identify some of these inefficiencies and to give suggestions on how to prevent and overcome those common situations in scientific code to improve the efficient use of available computational resources in a parallel homogeneous platform.

Abstract
This communication presents an evolutionary software prototype of a user-centered Highly Efficient Pipelined Framework, HEP-Frame, to aid the development of sustainable parallel scientific code with a flexible pipeline structure. HEP-Frame is the result of a tight collaboration between computational scientists and software engineers: it aims to improve scientists coding productivity, ensuring an efficient parallel execution on a wide set of multicore systems, with both HPC and HTC techniques. Current prototype complies with the requirements of an actual scientific code, includes desirable sustainability features and supports at compile time additional plugin interfaces for other scientific fields. The porting and development productivity was assessed and preliminary efficiency results are promising.