Related papers: Towards High Performance Computing (Hpc) Through P…
Future applications demand more performance, but technology advances have been faltering. A promising approach to further improve computer system performance under energy constraints is to employ hardware accelerators. Already today, mobile…
Multicore parallel programming has some very difficult problems such as deadlocks during synchronizations and race conditions brought by concurrency. Added to the difficulty is the lack of a simple, well-accepted computing model for…
As quantum computers continue to improve and support larger, more complex computations, smart control hardware and compilers are needed to efficiently leverage the capabilities of these systems. This paper introduces a novel approach to…
Many types of geospatial analyses are computationally complex, involving, for example, solution processes that require numerous iterations or combinatorial comparisons. This complexity has motivated the application of high performance…
Parallel computing has established itself as another standard method for applied research and data analysis. The R system, being internally constrained to mostly singly-threaded operations, can nevertheless be used along with different…
Fault tolerance overhead of high performance computing (HPC) applications is becoming critical to the efficient utilization of HPC systems at large scale. HPC applications typically tolerate fail-stop failures by checkpointing. Another…
We study the problem of computing a full Conjunctive Query in parallel using $p$ heterogeneous machines. Our computational model is similar to the MPC model, but each machine has its own cost function mapping from the number of bits it…
R is a robust open-source programming language mainly used for statistical computing . Many areas of statistical research are experiencing rapid growth in the size of data sets. Methodological advances drive increased use of simulations. A…
Parallelization has become a cornerstone of modern computing, influencing everything from high performance supercomputers to everyday mobile devices. This paper presents a comprehensive guide on the fundamentals of parallelization that…
Recent advances in computing architectures and networking are bringing parallel computing systems to the masses so increasing the number of potential users of these kinds of systems. In particular, two important technological evolutions are…
In this paper I describe some results on the use of virtual processors technology for parallelize some SPMD computational programs in a cluster environment. The tested technology is the INTEL Hyper Threading on real processors, and the…
In the past decade, high performance compute capabilities exhibited by heterogeneous GPGPU platforms have led to the popularity of data parallel programming languages such as CUDA and OpenCL. Such languages, however, involve a steep…
Since the days of OpenMP 1.0 computer hardware has become more complex, typically by specializing compute units for coarse- and fine-grained parallelism in incrementally deeper hierarchies of parallelism. Newer versions of OpenMP reacted by…
The use of High Performance Computing (HPC) to compliment urgent decision making in the event of disasters is an important future potential use of supercomputers. However, the usage modes involved are rather different from how HPC has been…
Applications like Big Data, Machine Learning, Deep Learning and even other Engineering and Scientific research requires a lot of computing power; making High-Performance Computing (HPC) an important field. But access to Supercomputers is…
The modern trend in High-Performance Computing (HPC) involves the use of accelerators such as Graphics Processing Units (GPUs) alongside Central Processing Units (CPUs) to speed up numerical operations in various applications. Leading…
The growing complexity of real-world systems necessitates interdisciplinary solutions to confront myriad challenges in modeling, analysis, management, and control. To meet these demands, the parallel systems method rooted in Artificial…
A key motivation in the development of Distributed Model Predictive Control (DMPC) is to accelerate centralized Model Predictive Control (MPC) for large-scale systems. DMPC has the prospect of scaling well by parallelizing computations…
At present, the mostly used and developed mechanism is hardware virtualization which provides a common platform to run multiple operating systems and applications in independent partitions. More precisely, it is all about resource…
Research processes often rely on high-performance computing (HPC), but HPC is often seen as antithetical to "reproducibility": one would have to choose between software that achieves high performance, and software that can be deployed in a…