arXiv subjects

Aditya Bhosale

Publications and source records attributed to Aditya Bhosale.

8 recordsLinked to original sources

arXiv2026

Efficient and Portable Support for Overdecomposition on Distributed Memory GPGPU Platforms

Overdecomposition has emerged as a powerful and sometimes essential technique in parallel programming. Many application domains or frameworks, including those based on adaptive mesh refinements, or tree codes use it. Charm++ is a parallel programming system which has demonstrated the utility of overdecomposition for many applications and in multiple contexts. However, the emergence of GPGPUs as a dominant compute component has created some real and perceived challenges for this paradigm, especially regarding the higher overhead brought about by overpartitioning -- having multiple objects assigned to the same GPGPU device. We address this issue as well as the issue of portability by developing techniques and software that demonstrate that overdecomposition can be efficiently and productively supported on combinations of GPU vendor types, and interconnection networks.

cs.DC↗

arXiv2026

Towards an Adaptive Runtime System for Cloud-Native HPC

The ongoing convergence of HPC and cloud computing presents a fundamental challenge: HPC applications, designed for static and homogeneous supercomputers, are ill-suited for the dynamic, heterogeneous, and volatile nature of the cloud. Traditional parallel programming models like MPI struggle to leverage key cloud advantages, such as resource elasticity and low-cost spot instances, while also failing to address challenges like performance variability and processor heterogeneity. This paper demonstrates how the asynchronous, message-driven paradigm of the Charm++ parallel runtime system can bridge this gap. We present a set of tools and strategies that enable HPC applications to run efficiently and resiliently on dynamic cloud infrastructure across both CPU and GPU resources. Our work makes two key contributions. First, we demonstrate that rate-aware load balancing in Charm++ improves performance for applications running on heterogeneous CPU and GPU instances on the cloud. We further demonstrate how core Charm++ principles mitigate performance degradation from common cloud challenges like network contention and processor performance variability, which are exacerbated by the tightly coupled, globally synchronized nature of many science and engineering applications. Second, we extend an existing resource management framework to support GPU and CPU spot instances with minimal interruption overhead. Together, these contributions provide a robust framework for adapting HPC applications to achieve efficient, resilient, and cost-effective performance on the cloud.

cs.DC↗

arXiv2025

An Adaptive Distributed Stencil Abstraction for GPUs

The scientific computing ecosystem in Python is largely confined to single-node parallelism, creating a gap between high-level prototyping in NumPy and high-performance execution on modern supercomputers. The increasing prevalence of hardware accelerators and the need for energy efficiency have made resource adaptivity a critical requirement, yet traditional HPC abstractions remain rigid. To address these challenges, we present an adaptive, distributed abstraction for stencil computations on multi-node GPUs. This abstraction is built using CharmTyles, a framework based on the adaptive Charm++ runtime, and features a familiar NumPy-like syntax to minimize the porting effort from prototype to production code. We showcase the resource elasticity of our abstraction by dynamically rescaling a running application across a different number of nodes and present a performance analysis of the associated overheads. Furthermore, we demonstrate that our abstraction achieves significant performance improvements over both a specialized, high-performance stencil DSL and a generalized NumPy replacement.

cs.DC↗

arXiv2025

An Elastic Job Scheduler for HPC Applications on the Cloud

The last few years have seen an increase in adoption of the cloud for running HPC applications. The pay-as-you-go cost model of these cloud resources has necessitated the development of specialized programming models and schedulers for HPC jobs for efficient utilization of cloud resources. A key aspect of efficient utilization is the ability to rescale applications on the fly to maximize the utilization of cloud resources. Most commonly used parallel programming models like MPI have traditionally not supported autoscaling either in a cloud environment or on supercomputers. While more recent work has been done to implement this functionality in MPI, it is still nascent and requires additional programmer effort. Charm++ is a parallel programming model that natively supports dynamic rescaling through its migratable objects paradigm. In this paper, we present a Kubernetes operator to run Charm++ applications on a Kubernetes cluster. We then present a priority-based elastic job scheduler that can dynamically rescale jobs based on the state of a Kubernetes cluster to maximize cluster utilization while minimizing response time for high-priority jobs. We show that our elastic scheduler, with the ability to rescale HPC jobs with minimal overhead, demonstrates significant performance improvements over traditional static schedulers.

cs.DC↗

arXiv2022

A Multichannel RF Transceiver Array with mixed L-C loop and microstrip elements for Foot/Ankle MR Imaging at 7T

It is technically challenging to design efficient transceiver coil arrays for foot and ankle imaging at ultrahigh fields due to the irregular geometry of the anatomy. Shortened wavelength because of the high operation frequency at ultrahigh fields increases phase variation, leading to inhomogeneous B1 distribution in the imaging sample. In this work, we propose a hybrid design with mixed L-C loops and microstrip resonators for multichannel foot/ankle transceiver arrays at the ultrahigh field of 7T. The proposed transceiver array consists of 14 microstrip resonators and 5 L-C loop coils to cover the entire region of interest of the irregular-shaped foot/ankle with a relatively uniform B1 distribution at 7T. The feasibility and field behavior of the proposed design are systematically investigated numerically.

physics.med-ph↗

arXiv2022

High Dielectric Sheet to reduce Electric Fields and Flatten Magnetic Fields in Self-decoupled Radiofrequency coils for MR Imaging

High impedance RF coils, such as self-decoupled coils, reduce the electromagnetic coupling between the coil elements and eliminates the use of complex decoupling technologies. Although the high impedance design promises excellent decoupling between the coil elements, it also results in high electric fields across the RF coil, leading to potential safety problems during imaging. It also causes B1 field asymmetry, ultimately leading to difficulties in imaging quantification. In this study, we propose and investigate using a high dielectric sheet to reduce the electric fields across the coil and achieve excellent electromagnetic decoupling among the coil elements, thereby ensuring safer MRI at ultra-high fields and maintaining high imaging performance.

physics.med-ph↗

arXiv2022

Design of a 13-Channel Hybrid Array System for Foot/Ankle Magnetic Resonance Imaging at 7T/300MHz

Microstrip lines are being used in MR applications due to their unique properties, such as reduced radiation loss, high-frequency capability, and reduced perturbation of sample loading to the RF coil compared to conventional coils. Here, we present the design of the 13-channel hybrid array consisting of 12 Microstrips, 1 volume half birdcage coil placed on the foot/ankle phantom, and high permittivity materials to cover the maximum area of the subject at 7T/300MHz. We demonstrate using electromagnetic simulations, magnetic field distribution, SAR performance, and the coupling performance of the array elements. This work provides an ultrahigh field multichannel RF solution to lower extremity MR imaging with excellent imaging coverage and field uniformity.

physics.med-ph↗

arXiv2020

PySPH: a Python-based framework for smoothed particle hydrodynamics

PySPH is an open-source, Python-based, framework for particle methods in general and Smoothed Particle Hydrodynamics (SPH) in particular. PySPH allows a user to define a complete SPH simulation using pure Python. High-performance code is generated from this high-level Python code and executed on either multiple cores, or on GPUs, seamlessly. It also supports distributed execution using MPI. PySPH supports a wide variety of SPH schemes and formulations. These include, incompressible and compressible fluid flow, elastic dynamics, rigid body dynamics, shallow water equations, and other problems. PySPH supports a variety of boundary conditions including mirror, periodic, solid wall, and inlet/outlet boundary conditions. The package is written to facilitate reuse and reproducibility. This paper discusses the overall design of PySPH and demonstrates many of its features. Several example results are shown to demonstrate the range of features that PySPH provides.

physics.comp-ph↗