arXiv subjects

Brian Kelley

Publications and source records attributed to Brian Kelley.

12 recordsLinked to original sources

arXiv2025

LAPIS: A Performance Portable, High Productivity Compiler Framework

Portability, performance, and productivity are three critical dimensions for evaluating a programming model or compiler infrastructure. Several modern programming models for computational science focus on performance and portability. On the other end, several machine learning focused programming models focus on portability and productivity. A clear solution that is strong in all three dimensions has yet to emerge. A second related problem arises when use cases from computational science converge with machine learning. The disparate popular frameworks of these fields require programmers to manually integrate codes written in different frameworks. Finally, several programming frameworks lack easy options for extensibility as any new computer architecture change require complex changes to the programming models. We present LAPIS, an MLIR-based compiler that addresses all three of these challenges. We demonstrate that LAPIS can automatically lower sparse and dense linear algebra kernels from computational science and artificial intelligence use cases. We also show how LAPIS facilitates the integration of codes between PyTorch and Kokkos. We compare kernel performance with the default MLIR implementations on diverse architectures to demonstrate portability. By developing a dialect that is built on the principles of the Kokkos ecosystem, LAPIS also allows extensibility of the framework to new architectures.

cs.DC↗

arXiv2025

Performance Portable Gradient Computations Using Source Transformation

Derivative computation is a key component of optimization, sensitivity analysis, uncertainty quantification, and nonlinear solvers. Automatic differentiation (AD) is a powerful technique for evaluating such derivatives, and in recent years, has been integrated into programming environments such as Jax, PyTorch, and TensorFlow to support derivative computations needed for training of machine learning models, resulting in widespread use of these technologies. The C++ language has become the de facto standard for scientific computing due to numerous factors, yet language complexity has made the adoption of AD technologies for C++ difficult, hampering the incorporation of powerful differentiable programming approaches into C++ scientific simulations. This is exacerbated by the increasing emergence of architectures such as GPUs, which have limited memory capabilities and require massive thread-level concurrency. Portable scientific codes rely on domain specific programming models such as Kokkos making AD for such codes even more complex. In this paper, we will investigate source transformation-based automatic differentiation using Clad to automatically generate portable and efficient gradient computations of Kokkos-based code. We discuss the modifications of Clad required to differentiate Kokkos abstractions. We will illustrate the feasibility of our proposed strategy by comparing the wall-clock time of the generated gradient code with the wall-clock time of the input function on different cutting edge GPU architectures such as NVIDIA H100, AMD MI250x, and Intel Ponte Vecchio GPU. For these three architectures and for the considered example, evaluating up to 10 000 entries of the gradient only took up to 2.17x the wall-clock time of evaluating the input function.

cs.MS↗

arXiv2025

Mobile Distributed MIMO (MD-MIMO) for NextG: Mobility Meets Cooperation in Distributed Arrays

Distributed multiple-input multiple-output (D\mbox{-}MIMO) is a promising technology to realize the promise of massive MIMO gains by fiber-connecting the distributed antenna arrays, thereby overcoming the form factor limitations of co-located MIMO. In this paper, we introduce the concept of mobile D-MIMO (MD-MIMO) network, a further extension of the D-MIMO technology where distributed antenna arrays are connected to the base station with a wireless link allowing all radio network nodes to be mobile. This approach significantly improves deployment flexibility and reduces operating costs, enabling the network to adapt to the highly dynamic nature of next-generation (NextG) networks. We discuss use cases, system design, network architecture, and the key enabling technologies for MD-MIMO. Furthermore, we investigate a case study of MD-MIMO for vehicular networks, presenting detailed performance evaluations for both downlink and uplink. The results show that an MD-MIMO network can provide substantial improvements in network throughput and reliability.

eess.SP↗

arXiv2025

Trilinos: Enabling Scientific Computing Across Diverse Hardware Architectures at Scale

Trilinos is a community-developed, open-source software framework that facilitates building large-scale, complex, multiscale, multiphysics simulation code bases for scientific and engineering problems. Since the Trilinos framework has undergone substantial changes to support new applications and new hardware architectures, this document is an update to ``An Overview of the Trilinos project'' by Heroux et al. (ACM Transactions on Mathematical Software, 31(3):397-423, 2005). It describes the design of Trilinos, introduces its new organization in product areas, and highlights established and new features available in Trilinos. Particular focus is put on the modernized software stack based on the Kokkos ecosystem to deliver performance portability across heterogeneous hardware architectures. This paper also outlines the organization of the Trilinos community and the contribution model to help onboard interested users and contributors.

cs.MS↗

arXiv2022

Parallel, Portable Algorithms for Distance-2 Maximal Independent Set and Graph Coarsening

Given a graph, finding the distance-2 maximal independent set (MIS-2) of the vertices is a problem that is useful in several contexts such as algebraic multigrid coarsening or multilevel graph partitioning. Such multilevel methods rely on finding the independent vertices so they can be used as seeds for aggregation in a multilevel scheme. We present a parallel MIS-2 algorithm to improve performance on modern accelerator hardware. This algorithm is implemented using the Kokkos programming model to enable performance portability. We demonstrate the portability of the algorithm and the performance on a variety of architectures (x86/ARM CPUs and NVIDIA/AMD GPUs). The resulting algorithm is also deterministic, producing an identical result for a given input across all of these platforms. The new MIS-2 implementation outperforms implementations in state of the art libraries like CUSP and ViennaCL by 3-8x while producing similar quality results. We further demonstrate the benefits of this approach by developing parallel graph coarsening scheme for two different use cases. First, we develop an algebraic multigrid (AMG) aggregation scheme using parallel MIS-2 and demonstrate the benefits as opposed to previous approaches used in the MueLu multigrid package in Trilinos. We also describe an approach for implementing a parallel multicolor "cluster" Gauss-Seidel preconditioner using this MIS-2 coarsening, and demonstrate better performance with an efficient, parallel, multicolor Gauss-Seidel algorithm.

cs.DC↗

arXiv2021

Two-Stage Gauss--Seidel Preconditioners and Smoothers for Krylov Solvers on a GPU cluster

Gauss-Seidel (GS) relaxation is often employed as a preconditioner for a Krylov solver or as a smoother for Algebraic Multigrid (AMG). However, the requisite sparse triangular solve is difficult to parallelize on many-core architectures such as graphics processing units (GPUs). In the present study, the performance of the traditional GS relaxation based on a triangular solve is compared with two-stage variants, replacing the direct triangular solve with a fixed number of inner Jacobi-Richardson (JR) iterations. When a small number of inner iterations is sufficient to maintain the Krylov convergence rate, the two-stage GS (GS2) often outperforms the traditional algorithm on many-core architectures. We also compare GS2 with JR. When they perform the same number of flops for SpMV (e.g. three JR sweeps compared to two GS sweeps with one inner JR sweep), the GS2 iterations, and the Krylov solver preconditioned with GS2, may converge faster than the JR iterations. Moreover, for some problems (e.g. elasticity), it was found that JR may diverge with a damping factor of one, whereas two-stage GS may improve the convergence with more inner iterations. Finally, to study the performance of the two-stage smoother and preconditioner for a practical problem, %(e.g. using tuned damping factors), these were applied to incompressible fluid flow simulations on GPUs.

math.NA↗

arXiv2021

Enabling Content-Centric Device-to-Device Communication in the Millimeter-Wave Band

The growth in wireless traffic and mobility of devices have congested the core network significantly. This bottleneck, along with spectrum scarcity, made the conventional cellular networks insufficient for the dissemination of large contents. In this paper, we propose a novel scheme that enables efficient initialization of CCN-based D2D networks in the mmWave band through addressing decentralized D2D peer association and antenna beamwidth selection. The proposed scheme considers mmWave characteristics such as directional communication and blockage susceptibility. We propose a heuristic peer association algorithm to associate D2D users using context information, including link stability time and content availability. The performance of the proposed scheme in terms of data throughput and transmission efficiency is evaluated through extensive simulations. Simulation results show that the proposed scheme improves network performance significantly and outperforms other methods in the literature.

cs.NI↗

arXiv2021

Kokkos Kernels: Performance Portable Sparse/Dense Linear Algebra and Graph Kernels

As hardware architectures are evolving in the push towards exascale, developing Computational Science and Engineering (CSE) applications depend on performance portable approaches for sustainable software development. This paper describes one aspect of performance portability with respect to developing a portable library of kernels that serve the needs of several CSE applications and software frameworks. We describe Kokkos Kernels, a library of kernels for sparse linear algebra, dense linear algebra and graph kernels. We describe the design principles of such a library and demonstrate portable performance of the library using some selected kernels. Specifically, we demonstrate the performance of four sparse kernels, three dense batched kernels, two graph kernels and one team level algorithm.

cs.MS↗

arXiv2019

Analyzing Learned Molecular Representations for Property Prediction

Advancements in neural machinery have led to a wide range of algorithmic solutions for molecular property prediction. Two classes of models in particular have yielded promising results: neural networks applied to computed molecular fingerprints or expert-crafted descriptors, and graph convolutional neural networks that construct a learned molecular representation by operating on the graph structure of the molecule. However, recent literature has yet to clearly determine which of these two methods is superior when generalizing to new chemical space. Furthermore, prior research has rarely examined these new models in industry research settings in comparison to existing employed models. In this paper, we benchmark models extensively on 19 public and 16 proprietary industrial datasets spanning a wide variety of chemical endpoints. In addition, we introduce a graph convolutional model that consistently matches or outperforms models using fixed molecular descriptors as well as previous graph neural architectures on both public and proprietary datasets. Our empirical findings indicate that while approaches based on these representations have yet to reach the level of experimental reproducibility, our proposed model nevertheless offers significant improvements over models currently used in industrial workflows.

cs.LG↗

arXiv2019

Device-to-Device Communications in Millimeter Wave Band: Impact of Beam Alignment Error

Exploiting the millimeter wave (mmWave) band has recently attracted considerable attention as a potential solution to widespread deployment of device-to-device (D2D) communication challenges, namely, spectrum scarcity and interference. However, its directional nature makes the utilization of mmWave band a challenging task as it requires careful beam alignment between the D2D transmitter and receiver. In this paper, we investigate the impact of inaccurate angle-of-arrival (AoA) estimation as a beam alignment impairment on the performance of a directional mmWave D2D network. We have used tools from stochastic geometry to quantify the signal-to-interference-plus-noise ratio (SINR) coverage probability in the presence of beam misalignment, which can be applied to evaluate D2D network performance. Moreover, the analytical results are verified to be reliable and effective through extensive simulations. Finally, the coverage probability of the D2D network with erroneous beam alignment is compared to the network with perfect beam alignment. The numerical results indicate that the beam misalignment can lead to significant losses in the network's coverage probability.

eess.SP↗

arXiv2018

Device-to-Device Communications in the Millimeter Wave Band: A Novel Distributed Mechanism

In spite of its potential advantages, the large-scale implementation of the device-to-device (D2D) communications has yet to be realized, mainly due to severe interference and lack of enough bandwidth in the microwave ($μ$W) band. Recently, exploiting the millimeter wave (mmW) band for D2D communications has attracted considerable attention as a potential solution to these challenges. However, its severe sensitivity to blockage along with its directional nature make the utilization of the mmW band a challenging task as it requires line-of-sight (LOS) link detection and careful beam alignment between the D2D transceivers. In this paper, we propose a novel distributed mechanism which enables the D2D devices to discover unblocked LOS links for the mmW band communication. Moreover, as such LOS links are not always available, the proposed mechanism allows the D2D devices to switch to the $μ$W band if necessary. In addition, the proposed mechanism detects the direction of the LOS links to perform the beam alignment. We have used tools from stochastic geometry to evaluate the performance of the proposed mechanism in terms of the signal-to-interference-plus-noise ratio (SINR) coverage probability. The performance of the proposed algorithm is then compared to the one of the single band (i.e., $μ$W/mmW) communication. The simulation results show that the proposed mechanism considerably outperforms the single band communication.

cs.IT↗

arXiv2016

Jamming in the Internet of Things: A Game-Theoretic Perspective

Due to its scale and largely interconnected nature, the Internet of Things (IoT) will be vulnerable to a number of security threats that range from physical layer attacks to network layer attacks. In this paper, a novel anti-jamming strategy for OFDM-based IoT systems is proposed which enables an IoT controller to protect the IoT devices against a malicious radio jammer. The interaction between the controller node and the jammer is modeled as a Colonel Blotto game with continuous and asymmetric resources in which the IoT controller, acting as defender, seeks to thwart the jamming attack by distributing its power among the subcarries in a smart way to decrease the aggregate bit error rate (BER) caused by the jammer. The jammer, on the other hand, aims at disrupting the system performance by allocating jamming power to different frequency bands. To solve the game, an evolutionary algorithm is proposed which can find a mixed-strategy Nash equilibrium of the Blotto game. Simulation results show that the proposed algorithm enables the IoT controller to maintain the BER above an acceptable threshold, thereby preserving the IoT network performance in the presence of malicious jamming.

cs.IT↗