arXiv subjects

Ahmed Helal

Publications and source records attributed to Ahmed Helal.

5 recordsLinked to original sources

arXiv2026

$g$MAGNUS: Fast SpGEMM on GPUs for Irregular Matrices via Hierarchical Multisplit

We present $g$MAGNUS, a novel algorithm for sparse matrix-matrix multiplication (SpGEMM) of irregular matrices on GPUs. Such matrices often contain many heavy rows, those with large intermediate products that force local memory accumulators to spill to global memory. $g$MAGNUS addresses this by computing an intra-row reordering of intermediate products, subdividing heavy rows into independent chunks that can be accumulated completely in local memory. This reordering uses novel outer product and hierarchical multisplit operations. The algorithm is input- and system-aware, automatically determining the number of chunks and multisplit levels based on the input matrix dimensions and local memory size. Experimental results on two extensive datasets show that $g$MAGNUS achieves a geometric-mean speedup of 1.81 to 7.62$\times$ over five leading algorithms (including MKL and cuSPARSE) on Intel Ponte Vecchio and NVIDIA H200. Additionally, the core kernels of $g$MAGNUS are evaluated, achieving near-peak performance compared to their theoretical upper bound.

cs.DC↗

arXiv2024

KGLiDS: A Platform for Semantic Abstraction, Linking, and Automation of Data Science

In recent years, we have witnessed the growing interest from academia and industry in applying data science technologies to analyze large amounts of data. In this process, a myriad of artifacts (datasets, pipeline scripts, etc.) are created. However, there has been no systematic attempt to holistically collect and exploit all the knowledge and experiences that are implicitly contained in those artifacts. Instead, data scientists recover information and expertise from colleagues or learn via trial and error. Hence, this paper presents a scalable platform, KGLiDS, that employs machine learning and knowledge graph technologies to abstract and capture the semantics of data science artifacts and their connections. Based on this information, KGLiDS enables various downstream applications, such as data discovery and pipeline automation. Our comprehensive evaluation covers use cases in data discovery, data cleaning, transformation, and AutoML. It shows that KGLiDS is significantly faster with a lower memory footprint than the state-of-the-art systems while achieving comparable or better accuracy.

cs.LG↗

arXiv2021

DiaLex: A Benchmark for Evaluating Multidialectal Arabic Word Embeddings

Word embeddings are a core component of modern natural language processing systems, making the ability to thoroughly evaluate them a vital task. We describe DiaLex, a benchmark for intrinsic evaluation of dialectal Arabic word embedding. DiaLex covers five important Arabic dialects: Algerian, Egyptian, Lebanese, Syrian, and Tunisian. Across these dialects, DiaLex provides a testbank for six syntactic and semantic relations, namely male to female, singular to dual, singular to plural, antonym, comparative, and genitive to past tense. DiaLex thus consists of a collection of word pairs representing each of the six relations in each of the five dialects. To demonstrate the utility of DiaLex, we use it to evaluate a set of existing and new Arabic word embeddings that we developed. Our benchmark, evaluation code, and new word embedding models will be publicly available.

cs.AI↗

arXiv2016

Simultaneous rheo-electric measurements of strongly conductive complex fluids

We introduce a novel apparatus designed for stress-controlled rheometers to perform simultaneous rheological and electrical measurements on strongly conductive complex fluids under shear. By means of a non-toxic liquid metal at room temperature, the electrical connection to the rotating shaft is completed with minimal additional mechanical friction, allowing for simultaneous stress measurements as low as 1 Pa. We use the capabilities of this design to perform an extensive set of rheo-electric experiments on gels formulated from attractive carbon black particles, at concentrations ranging from 4 to 15% wt. First, experiments on gels at rest prepared with different shear history show a robust power-law scaling between the elastic modulus $G'_0$ and the conductivity $σ_0$ of the gels, i.e. $G'_0 \sim σ_0^α$, with $α=1.65 \pm 0.04$ independently of the gel concentration. Second, conductivity measurements performed simultaneously with creep experiments reveal for the first time that plastic events take place in the bulk while the shear rate decreases as a weak power law of time in the early stage of the experiment. The subsequent evolution of the conductivity and shear rate allows us to propose a local yielding scenario that is in agreement with previous velocimetry measurements. Finally, we determine the constitutive rheological and electrical behavior of carbon black gels. Corrections first introduced for mechanical measurements are carefully extended to electrical measurements to accurately distinguish between bulk and surface contributions to the conductivity. As an illustrative example, we examine the constitutive rheo-electric properties of five carbon black gels of different grades, and demonstrate the relevance of the novel rheo-electric apparatus as a versatile characterization tool for strongly conductive complex fluids and their applications.

cond-mat.mtrl-sci↗

arXiv2016

Coupled dynamics of flow, microstructure, and conductivity in sheared suspensions

We propose a model for the evolution of the conductivity tensor for a flowing suspension of electrically conductive particles. We use discrete particle numerical simulations together with a continuum physical framework to construct an evolution law for the suspension microsutructure during flow. This model is then coupled with a relationship between the microstructure and the electrical conductivity tensor. The parameters of the joint model are fit experimentally using rheo- electrical conductivity measurements of carbon black suspensions under flow over a range of shear rates. The model is applied to the case of steady shearing as well as time-varying conductivity of unsteady flow experiments. We find that the model prediction agrees closely with the measured experimental data in all cases.

cond-mat.soft↗