arXiv subjects

Sunita Chandrasekaran

Publications and source records attributed to Sunita Chandrasekaran.

24 records · Page 2Linked to original sources

arXiv2021

OpenACC Acceleration of an Agent-Based Biological Simulation Framework

Computational biology has increasingly turned to agent-based modeling to explore complex biological systems. Biological diffusion (diffusion, decay, secretion, and uptake) is a key driver of biological tissues. GPU computing can vastly accelerate the diffusion and decay operators in the partial differential equations used to represent biological transport in an agent-based biological modeling system. In this paper, we utilize OpenACC to accelerate the diffusion portion of PhysiCell, a cross-platform agent-based biosimulation framework. We demonstrate an almost 40x speedup on the state-of-the-art NVIDIA A100 GPU compared to a serial run on AMD's EPYC 7742. We also demonstrate 9x speedup on the 64 core AMD EPYC 7742 multicore platform. By using OpenACC for both the CPUs and the GPUs, we maintain a single source code base, thus creating a portable yet performant solution. With the simulator's most significant computational bottleneck significantly reduced, we can continue cancer simulations over much longer times.

cs.DC↗

arXiv2021

Refactoring the MPS/University of Chicago Radiative MHD(MURaM) Model for GPU/CPU Performance Portability Using OpenACC Directives

The MURaM (Max Planck University of Chicago Radiative MHD) code is a solar atmosphere radiative MHD model that has been broadly applied to solar phenomena ranging from quiet to active sun, including eruptive events such as flares and coronal mass ejections. The treatment of physics is sufficiently realistic to allow for the synthesis of emission from visible light to extreme UV and X-rays, which is critical for a detailed comparison with available and future multi-wavelength observations. This component relies critically on the radiation transport solver (RTS) of MURaM; the most computationally intensive component of the code. The benefits of accelerating RTS are multiple fold: A faster RTS allows for the regular use of the more expensive multi-band radiation transport needed for comparison with observations, and this will pave the way for the acceleration of ongoing improvements in RTS that are critical for simulations of the solar chromosphere. We present challenges and strategies to accelerate a multi-physics, multi-band MURaM using a directive-based programming model, OpenACC in order to maintain a single source code across CPUs and GPUs. Results for a $288^3$ test problem show that MURaM with the optimized RTS routine achieves 1.73x speedup using a single NVIDIA V100 GPU over a fully subscribed 40-core Intel Skylake CPU node and with respect to the number of simulation points (in millions) per second, a single NVIDIA V100 GPU is equivalent to 69 Skylake cores. We also measure parallel performance on up to 96 GPUs and present weak and strong scaling results.

physics.space-ph↗

arXiv2020

Performance Assessment of OpenMP Compilers Targeting NVIDIA V100 GPUs

Heterogeneous systems are becoming increasingly prevalent. In order to exploit the rich compute resources of such systems, robust programming models are needed for application developers to seamlessly migrate legacy code from today's systems to tomorrow's. Over the past decade and more, directives have been established as one of the promising paths to tackle programmatic challenges on emerging systems. This work focuses on applying and demonstrating OpenMP offloading directives on five proxy applications. We observe that the performance varies widely from one compiler to the other; a crucial aspect of our work is reporting best practices to application developers who use OpenMP offloading compilers. While some issues can be worked around by the developer, there are other issues that must be reported to the compiler vendors. By restructuring OpenMP offloading directives, we gain an 18x speedup for the su3 proxy application on NERSC's Cori system when using the Clang compiler, and a 15.7x speedup by switching max reductions to add reductions in the laplace mini-app when using the Cray-llvm compiler on Cori.

cs.PF↗

arXiv2020

Future Directions of the Cyberinfrastructure for Sustained Scientific Innovation (CSSI) Program

The CSSI 2019 workshop was held on October 28-29, 2019, in Austin, Texas. The main objectives of this workshop were to (1) understand the impact of the CSSI program on the community over the last 9 years, (2) engage workshop participants in identifying gaps and opportunities in the current CSSI landscape, (3) gather ideas on the cyberinfrastructure needs and expectations of the community with respect to the CSSI program, and (4) prepare a report summarizing the feedback gathered from the community that can inform the future solicitations of the CSSI program. The workshop brought together different stakeholders interested in provisioning sustainable cyberinfrastructure that can power discoveries impacting the various fields of science and technology and maintaining the nation's competitiveness in the areas such as scientific software, HPC, networking, cybersecurity, and data/information science. The workshop served as a venue for gathering the community-feedback on the current state of the CSSI program and its future directions.

cs.CY↗

arXiv2019

Assessing Performance Implications of Deep Copy Operations via Microbenchmarking

As scientific frameworks become sophisticated, so do their data structures. Current data structures are no longer simple in design and they have been progressively complicated. The typical trend in designing data structures in scientific applications are basically nested data structures: pointing to a data structure within another one. Managing nested data structures on a modern heterogeneous system requires tremendous effort due to the separate memory space design. In this paper, we will discuss the implications of deep copy on data transfers on current heterogeneous. Then, we will discuss the two options that are currently available to perform the memory copy operations on complex structures and will introduce pointerchain directive that we proposed. Afterwards, we will introduce a set of extensive benchmarks to compare the available approaches. Our goal is to make our proposed benchmarks a basis to examine the efficiency of upcoming approaches that address the challenge of deep copy operations.

cs.DC↗

arXiv2018

Power and Energy-efficiency Roofline Model for GPUs

Energy consumption has been a great deal of concern in recent years and developers need to take energy-efficiency into account when they design algorithms. Their design needs to be energy-efficient and low-power while it tries to achieve attainable performance provided by underlying hardware. However, different optimization techniques have different effects on power and energy-efficiency and a visual model would assist in the selection process. In this paper, we extended the roofline model and provided a visual representation of optimization strategies for power consumption. Our model is composed of various ceilings regarding each strategy we included in our models. One roofline model for computational performance and one for memory performance is introduced. We assembled our models based on some optimization strategies for two widespread GPUs from NVIDIA: Geforce GTX 970 and Tesla K80.

cs.DC↗