Exascale Computing

Ubiquitous Computing is a paradigm containing the
characteristics to break away from the traditional desktop
computing system and turn to such computer systems where
everything is available & accessible everywhere through various
devices while keeping them effectively invisible. The
accomplishment of this vision requires a deep involvement of
multiple areas including software engineering. Software
engineering plays a major role in ubi-comp world; but the lack of
appropriate software engineering approaches is identified as a
major obstacle on the route to propose a general level framework
that should be adopted for all kinds of ubi-comp applications. The
very initial step which is still required for this vision is to identify all
potential software engineering challenges in the ubi-comp era. In
this paper we have made a survey of various ubi-comp applications
and extracted a number of software engineering challenges faced
by other researchers during the development of such applications.
This work provides a future direction for other researchers and
developers to make the process of providing an appropriate
framework for building ubi-comp application more effectively.

Over the last 20 years, the open-source community has provided more and more software on which the world’s high-performance computing systems depend for performance and productivity. The community has invested millions of dollars and years of effort to build key components. However, although the investments in these separate software elements have been tremendously valuable, a great deal of productivity has also been lost because of the lack of planning, coordination, and key integration of technologies necessary to make them work together smoothly and efficiently, both within individual petascale systems and between different systems. It seems clear that this completely uncoordinated development model will not provide the software needed to support the unprecedented parallelism required for peta/ exascale computation on millions of cores, or the flexibility required to exploit new hardware models and features, such as transactional memory, speculative execution, and graphics proces...

Rapidly changing computer architectures, such as those found at high-performance computing (HPC) facilities, present the need for mini-applications (miniapps) that capture essential algorithms used in large applications to test program performance and portability, aiding transitions to new systems. The COVID-19 pandemic has fueled a flurry of activity in computational drug discovery, including the use of supercomputers and GPU acceleration for massive virtual screens for therapeutics. Recent work targeting COVID-19 at the Oak Ridge Leadership Computing Facility (OLCF) used the GPU-accelerated program AutoDock-GPU to screen billions of compounds on the Summit supercomputer. In this paper we present the development of a new miniapp, miniAutoDock-GPU, that can be used to evaluate the performance and portability of GPU-accelerated protein-ligand docking programs on different computer architectures. These tests are especially relevant as facilities transition from petascale systems and prepare for upcoming exascale systems that will use a variety of GPU vendors. The key calculations, namely, the Lamarckian genetic algorithm combined with a local search using a Solis-Wets based random optimization algorithm, are implemented. We developed versions of the miniapp using several different programming models for GPU acceleration, including a version using the CUDA runtime API for NVIDIA GPUs, and the Kokkos middle-ware API which is facilitated by C++ template libraries. A third version, currently in progress, uses the HIP programming model. These efforts will help facilitate the transition to exascale systems for this important emerging HPC application, as well as its use on a wide range of heterogeneous platforms.

Over the last 20 years, the open-source community has provided more and more software on which the world’s high-performance computing systems depend for performance and productivity. The community has invested millions of dollars and years of effort to build key components. However, although the investments in these separate software elements have been tremendously valuable, a great deal of productivity has also been lost because of the lack of planning, coordination, and key integration of technologies necessary to make them work together smoothly and efficiently, both within individual petascale systems and between different systems. It seems clear that this completely uncoordinated development model will not provide the software needed to support the unprecedented parallelism required for peta/ exascale computation on millions of cores, or the flexibility required to exploit new hardware models and features, such as transactional memory, speculative execution, and graphics proces...

Over the last 20 years, the open-source community has provided more and more software on which the world’s high-performance computing systems depend for performance and productivity. The community has invested millions of dollars and years of effort to build key components. However, although the investments in these separate software elements have been tremendously valuable, a great deal of productivity has

In next decade, for exascale high computing power and speed, new high performance computing
(HPC) architectures, algorithms and corrections in existing technologies are expected. In order to
achieve HPC parallelism is becoming a core emphasizing point. Keeping in view the advantages
of parallelism, GPU is a unit that provides the better performance to achieve HPC in exascale
computing system. So far, many programming models have been introduced to program GPU like
CUDA, OpenGL, and OpenCL etc. and still there are number of limitations for these models that
are  required  a  deep  glance  to  fix  them.  In  order  to enhance  the  performance  in  GPU
programming  in  OpenGL,  we  have  proposed  an  OpenGL  based  testing  tool  architecture  for
exascale computing system. This testing architecture detects the errors from OpenGL code and
enforce to write the code in accurate way.