Related papers: Expanding IceCube GPU computing into the Clouds
Satellites have become more widely available due to the reduction in size and cost of their components. As a result, there has been an advent of smaller organizations having the ability to deploy satellites with a variety of data-intensive…
Graph-based ANNS algorithms have gained increasing research interest and market adoption due to their efficiency and accuracy in retrieval. Existing approaches primarily rely on CPUs for graph index construction and retrieval, but this…
Commercial graphics processors (GPUs) have high compute capacity at very low cost, which makes them attractive for general purpose scientific computing. In this paper we show how graphics processors can be used for N-body simulations to…
The era of GPU-powered data analytics has arrived. In this paper, we argue that recent advances in hardware (e.g., larger GPU memory, faster interconnect and IO, and declining cost) and software (e.g., composable data systems and mature…
Cloud computing changed the way of computing as utility services offered through public network. Selecting multiple providers for various computational requirements improves performance and minimizes cost of cloud services than choosing a…
GPU (graphics processing unit) has been used for many data-intensive applications. Among them, deep learning systems are one of the most important consumer systems for GPU nowadays. As deep learning applications impose deeper and larger…
Lattice QCD calculations were one of the first applications to show the potential of GPUs in the area of high performance computing. Our interest is to find ways to effectively use GPUs for lattice calculations using the overlap operator.…
Fine-grained workload and resource balancing is the key to high performance for regular and irregular computations on the GPUs. In this dissertation, we conduct an extensive survey of existing load-balancing techniques to build an…
IceCube is a one-gigaton instrument located at the geographic South Pole, designed to detect cosmic neutrinos, iden- tify the particle nature of dark matter, and study high-energy neutrinos themselves. Simulation of the IceCube detector and…
GPUs are essential to accelerating the latency-sensitive deep neural network (DNN) inference workloads in cloud datacenters. To fully utilize GPU resources, spatial sharing of GPUs among co-located DNN inference workloads becomes…
Hardware peripherals such as GPUs and FPGAs are commonly available in server-grade computing to accelerate specific compute tasks, from database queries to machine learning. CSPs have integrated these accelerators into their infrastructure…
To help understand our universe better, researchers and scientists currently run extreme-scale cosmology simulations on leadership supercomputers. However, such simulations can generate large amounts of scientific data, which often result…
Rapid advancements in cloud based platforms providing access to quantum computing capabilities have opened up several challenges for efficient usage of these highly delicate and costly devices. Although most of the current systems use a…
The rise of AI and the economic dominance of cloud computing have created a new nexus of innovation for high performance computing (HPC), which has a long history of driving scientific discovery. In addition to performance needs, scientific…
Fusion simulations have in the past required the use of leadership scale HPC resources to produce advances in physics. One such package is CGYRO, a premier multi-scale plasma turbulence simulation code. CGYRO is a typical HPC application…
Cloud computing has demonstrated itself to be a scalable and cost-efficient solution for many real-world applications. However, its modus operandi is not ideally suited to resource-constrained environments that are characterized by limited…
CPU is undoubtedly the most important resource of the computer system. Recent advances in software and system architecture have increased processing complexity, as computing is now distributed and parallel. CloudSim represents the…
Can cloud computing infrastructures provide HPC-competitive performance for scientific applications broadly? Despite prolific related literature, this question remains open. Answers are crucial for designing future systems and democratizing…
With the increasing number of Machine and Deep Learning applications in High Energy Physics, easy access to dedicated infrastructure represents a requirement for fast and efficient R&D. This work explores different types of cloud services…
We describe the technical effort used to process a voluminous high value human neuroimaging dataset on the Open Science Grid with opportunistic use of idle HPC resources to boost computing capacity more than 5-fold. With minimal software…