Search Results - "Kanakagiri, Raghavendra"

Scalable GW software for quasiparticle properties using OpenAtom by Kim, Minjung, Mandal, Subhasish, Mikida, Eric, Chandrasekar, Kavitha, Bohm, Eric, Jain, Nikhil, Li, Qi, Kanakagiri, Raghavendra, Martyna, Glenn J., Kale, Laxmikant, Ismail-Beigi, Sohrab

“…The GW method, which can describe electronic excitations accurately, is a powerful ab initio electronic structure technique in molecular, condensed matter and…”
Get full text

SkipCache: application aware cache management for chip multi‐processors by Warrier, Tripti S, Raghavendra, Kanakagiri, Mutyam, Madhu

“…With the advent of multiple cores on a single chip, it is common for the systems to have multi‐level caches. Multiple levels of cache reduce the pressure on…”
Get full text

Communication-Efficient Jaccard similarity for High-Performance Distributed Genome Comparisons by Besta, Maciej, Kanakagiri, Raghavendra, Mustafa, Harun, Karasikov, Mikhail, Ratsch, Gunnar, Hoefler, Torsten, Solomonik, Edgar

“…The Jaccard similarity index is an important measure of the overlap of two sets, widely used in machine learning, computational genomics, information…”
Get full text

Minimum Cost Loop Nests for Contraction of a Sparse Tensor with a Tensor Network by Kanakagiri, Raghavendra, Solomonik, Edgar

“…Sparse tensor decomposition and completion are common in numerous applications, ranging from machine learning to computational quantum chemistry. Typically,…”
Get full text

Parallel Minimum Spanning Forest Computation using Sparse Matrix Kernels by Baer, Tim, Kanakagiri, Raghavendra, Solomonik, Edgar

“…Formulations of graph algorithms using sparse linear algebra have yielded highly scalable distributed algorithms for problems such as connectivity and shortest…”
Get full text

Optimizing Distributed Tensor Contractions using Node-Aware Processor Grids by Irmler, Andreas, Kanakagiri, Raghavendra, Ohlmann, Sebastian T, Solomonik, Edgar, Grüneis, Andreas

“…We propose an algorithm that aims at minimizing the inter-node communication volume for distributed and memory-efficient tensor contraction schemes on modern…”
Get full text

Hardware Acceleration for Knowledge Graph Processing: Challenges & Recent Developments by Besta, Maciej, Gerstenberger, Robert, Iff, Patrick, Sonawane, Pournima, Luna, Juan Gómez, Kanakagiri, Raghavendra, Min, Rui, Mutlu, Onur, Hoefler, Torsten, Appuswamy, Raja, Mahony, Aidan O

“…Knowledge graphs (KGs) have achieved significant attention in recent years, particularly in the area of the Semantic Web as well as gaining popularity in other…”
Get full text

ProbGraph: High-Performance and High-Accuracy Graph Mining with Probabilistic Set Representations by Besta, Maciej, Miglioli, Cesare, Labini, Paolo Sylos, Tetek, Jakub, Iff, Patrick, Kanakagiri, Raghavendra, Ashkboos, Saleh, Janda, Kacper, Podstawski, Michal, Kwasniewski, Grzegorz, Gleinig, Niels, Vella, Flavio, Mutlu, Onur, Hoefler, Torsten

“…Important graph mining problems such as Clustering are computationally demanding. To significantly accelerate these problems, we propose ProbGraph: a graph…”
Get full text

High-Performance and Programmable Attentional Graph Neural Networks with Global Tensor Formulations by Besta, Maciej, Renc, Pawel, Gerstenberger, Robert, Labini, Paolo Sylos, Ziogas, Alexandros, Chen, Tiancheng, Gianinazzi, Lukas, Scheidl, Florian, Szenes, Kalman, Carigiet, Armon, Iff, Patrick, Kwasniewski, Grzegorz, Kanakagiri, Raghavendra, Ge, Chio, Jaeger, Sammy, Was, Jaroslaw, Vella, Flavio, Hoefler, Torsten

“…Graph attention models (A-GNNs), a type of Graph Neural Networks (GNNs), have been shown to be more powerful than simpler convolutional GNNs (C-GNNs). However,…”
Get full text

ProbGraph: High-Performance and High-Accuracy Graph Mining with Probabilistic Set Representations by Besta, Maciej, Miglioli, Cesare, Labini, Paolo Sylos, Tětek, Jakub, Iff, Patrick, Kanakagiri, Raghavendra, Ashkboos, Saleh, Janda, Kacper, Podstawski, Michal, Kwasniewski, Grzegorz, Gleinig, Niels, Vella, Flavio, Mutlu, Onur, Hoefler, Torsten

“…Proceedings of the ACM/IEEE International Conference on High Performance Computing, Networking, Storage and Analysis, November 2022 Important graph mining…”
Get full text

Communication-Efficient Jaccard Similarity for High-Performance Distributed Genome Comparisons by Besta, Maciej, Kanakagiri, Raghavendra, Mustafa, Harun, Karasikov, Mikhail, Rätsch, Gunnar, Hoefler, Torsten, Solomonik, Edgar

“…Proceedings of the 34st IEEE International Parallel and Distributed Processing Symposium (IPDPS'20), 2020 The Jaccard similarity index is an important measure…”
Get full text

Motif Prediction with Graph Neural Networks by Besta, Maciej, Grob, Raphael, Miglioli, Cesare, Bernold, Nicola, Kwasniewski, Grzegorz, Gjini, Gabriel, Kanakagiri, Raghavendra, Ashkboos, Saleh, Gianinazzi, Lukas, Dryden, Nikoli, Hoefler, Torsten

“…Proceedings of the 28th SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'22), 2022 Link prediction is one of the central problems in graph mining…”
Get full text

SISA: Set-Centric Instruction Set Architecture for Graph Mining on Processing-in-Memory Systems by Besta, Maciej, Kanakagiri, Raghavendra, Kwasniewski, Grzegorz, Ausavarungnirun, Rachata, Beránek, Jakub, Kanellopoulos, Konstantinos, Janda, Kacper, Vonarburg-Shmaria, Zur, Gianinazzi, Lukas, Stefan, Ioana, Luna, Juan Gómez, Copik, Marcin, Kapp-Schwoerer, Lukas, Di Girolamo, Salvatore, Konieczny, Marek, Blach, Nils, Mutlu, Onur, Hoefler, Torsten

“…Simple graph algorithms such as PageRank have been the target of numerous hardware accelerators. Yet, there also exist much more complex graph mining…”
Get full text