Boosting parallel influence-maximization Kernels for undirected networks with fusing and vectorization

Influence maximization (IM) is the problem of finding a seed vertex set which is expected to incur the maximum influence spread on a graph. It has various applications in practice such as devising an effective and efficient approach to disseminate information, news or ad within a social network. The problem is shown to be NP-hard and approximation algorithms with provable quality guarantees exist in the literature. However, these algorithms are computationally expensive even for medium-scaled graphs. Furthermore, graph algorithms usually suffer from spatial and temporal irregularities during memory accesses, and this adds an extra cost on top of the already expensive IM kernels. In this article we leverage fused sampling, memoization, and vectorization to restructure, parallelize and boost their performance on undirected networks. The proposed approach employs a pseudo-random function and performs multiple Monte-Carlo simulations in parallel to exploit the SIMD lanes effectively and efficiently. In addition, it significantly reduces the number of edge traversals, hence the amount of data brought from the memory, which is critical for almost all memory-bound graph kernels. We apply the proposed approach to the traditional MixGreedy algorithm and propose INFuseR-MG which is more than 3000\times3000× faster than the greedy approaches and can run on large graphs that have been considered as too large in the literature. For instance, the new algorithm runs in 2.09, 0.08, 0.36 seconds on graphs Amazon, NetHEP, NetPhy with 16 threads where the sequential baseline takes 141.3, 259.1 and 1725.2 seconds, respectively. To compare INFuseR-MG with the state-of-the-art approximation algorithms, we conduct a thorough experimental analysis with various influence settings. The results on real-life, undirected networks show that on 16 threads, INFuseR-MG is 2.3\times2.3×-173.8\times173.8× faster than state-of-the-art while being superior in terms of influence scores, and using a comparable amount of memory

Fast and high-quality influence maximization on multiple GPUs

Influence Maximization (IM) is a popular problem focusing on finding a seed vertex set in a graph that maximizes the expected number of vertices affected via diffusion under a given, usually probabilistic model. For most diffusion models used in practice, finding an optimal seed set of a given size is NP-Hard. Hence, approximation algorithms and heuristics are often proposed and used. The Greedy approach is one of the most frequently applied approximation approach employed for IM. Indeed, this Monte-Carlo-based approach performs remarkably well in terms of seed set quality, i.e., the number of affected vertices. However, it is impractical for real-life networks containing tens of millions of vertices due to its expensive simulation costs. Recently, parallel IM kernels running on CPUs and GPUs have been proposed in the literature. In this work, we propose SUPERFUSER, a blazing-fast, sketch-based Influence Maximization algorithm developed for multiple GPUs. SUPERFUSER uses hash-based fused sampling to process multiple simulations at the same time with minimal overhead. In addition, we propose a Sampling-Aware Sample-Space Split approach to partition the edges to multiple GPUs efficiently by exploiting the unique characteristics of the sampling process. Based on our experiments, SUPERFUSER is up to 6.31× faster than its nearest competitor on a single GPU. Furthermore, we achieve 6.8× speed-up on average using 8 GPUs over a single GPU performance, and thanks to our novel partitioning scheme, we can process extremely large-scale graphs in practice without sacrificing quality too much. As an example, SUPERFUSER can generate a high-quality seed set with 50 vertices for a graph having 1.8B edges in less than 15 seconds on 2 GPUs

Fast and error-adaptive influence maximization based on count-distinct sketches

Influence maximization (IM) is the problem of finding a seed vertex set that maximizes the expected number of vertices influenced under a given diffusion model. Due to the NP-Hardness of finding an optimal seed set, high-quality yet expensive approximation algorithms have been frequently used. In addition, lightweight, sketch-based approaches, which do not step-by-step simulate the influence process, have been proposed in the literature to cope with the scale of today's networks. In this work, we describe a fast, error-adaptive approach that leverages Count-Distinct sketches and hash-based fused sampling to avoid step-by-step simulations and estimate the number of influenced vertices throughout a diffusion. Furthermore, for faster estimations, the sketches of a vertex for consecutive simulations are stored adjacently in memory. This allows the proposed algorithm to estimate the number of influenced vertices for multiple simulations at once. For faster processing, the proposed method automatically rebuilds the sketches after observing estimation errors above a given threshold. Our experimental results show that the proposed algorithm yields seed sets with comparable quality while being up to 3,337× faster than a state-of-the-art, high-quality influence maximization algorithm. In addition, it is up to 63× faster than a sketch-based approach while producing seed sets with 2%–10% better influence scores

3-6 YAŞ ARASI ÇOCUKLARIN ALET YAPIMI VE ALET İNOVASYONUNDA BİLİŞSEL, DİLSEL VE SOSYAL SÜREÇLERİN DENEYSEL BİR ARAŞTIRMASI

Bu çalışmanın amacı 3-6 yaş arası çocukların alet yapımı (tool making) ve alet inovasyonu(tool innovation) süreçlerindeki bilişsel, dilsel ve sosyal faktörlerin ortaya çıkarılmasıdır. Bu amaç doğrultusunda deneysel bir çalışmayla bu süreçlerin araştırılması planlanmaktadır