12 citations · 32 across the 12 of their papers we have counts for
14 papers
FissionReady: Joint Workload and Power Scheduling for Data Centers Powered by Small Modular Reactors
Raghavendra Kanakagiri, Rohan Basu Roy, Yankai Jiang +2
Data centers are increasingly exploring small modular nuclear reactors (SMRs) as a carbon-free power source, but variable datacenter demand and negative grid prices require the SMR…
ThirstyFLOPS: Water Footprint Modeling and Analysis Toward Sustainable HPC Systems
Yankai Jiang, Raghavendra Kanakagiri, Rohan Basu Roy +1
High-performance computing (HPC) systems are becoming increasingly water-intensive due to their reliance on water-based cooling and the energy used in power generation. However, th…
ForgetMeNot: Understanding and Modeling the Impact of Forever Chemicals Toward Sustainable Large-Scale Computing
Rohan Basu Roy, Raghavendra Kanakagiri, Yankai Jiang +1
Fluorinated compounds, often referred to as forever chemicals, are critical in various steps of semiconductor fabrication like lithography, etching, chamber cleaning, and others. F…
Performance-Driven Optimization of Parallel Breadth-First Search
Marati Bhaskar, Raghavendra Kanakagiri
Breadth-first search (BFS) is a fundamental graph algorithm that presents significant challenges for parallel implementation due to irregular memory access patterns, load imbalance…
WaterWise: Co-optimizing Carbon- and Water-Footprint Toward Environmentally Sustainable Cloud Computing
Yankai Jiang, Rohan Basu Roy, Raghavendra Kanakagiri +1
The carbon and water footprint of large-scale computing systems poses serious environmental sustainability risks. In this study, we discover that, unfortunately, carbon and water s…
Hardware Acceleration for Knowledge Graph Processing: Challenges & Recent Developments
Maciej Besta, Robert Gerstenberger, Patrick Iff +9
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 application domains…
Optimizing Distributed Tensor Contractions using Node-Aware Processor Grids
Andreas Irmler, Raghavendra Kanakagiri, Sebastian T. Ohlmann +2
We propose an algorithm that aims at minimizing the inter-node communication volume for distributed and memory-efficient tensor contraction schemes on modern multi-core compute nod…
Minimum Cost Loop Nests for Contraction of a Sparse Tensor with a Tensor Network
Raghavendra Kanakagiri, Edgar Solomonik
Sparse tensor decomposition and completion are common in numerous applications, ranging from machine learning to computational quantum chemistry. Typically, the main bottleneck in…
ProbGraph: High-Performance and High-Accuracy Graph Mining with Probabilistic Set Representations
Maciej Besta, Cesare Miglioli, Paolo Sylos Labini +11
Important graph mining problems such as Clustering are computationally demanding. To significantly accelerate these problems, we propose ProbGraph: a graph representation that enab…
Parallel Minimum Spanning Forest Computation using Sparse Matrix Kernels
Tim Baer, Raghavendra Kanakagiri, Edgar Solomonik
Formulations of graph algorithms using sparse linear algebra have yielded highly scalable distributed algorithms for problems such as connectivity and shortest path computation. We…
Motif Prediction with Graph Neural Networks
Maciej Besta, Raphael Grob, Cesare Miglioli +8
Link prediction is one of the central problems in graph mining. However, recent studies highlight the importance of higher-order network analysis, where complex structures called m…
SISA: Set-Centric Instruction Set Architecture for Graph Mining on Processing-in-Memory Systems
Maciej Besta, Raghavendra Kanakagiri, Grzegorz Kwasniewski +15
Simple graph algorithms such as PageRank have been the target of numerous hardware accelerators. Yet, there also exist much more complex graph mining algorithms for problems such a…
Communication-Efficient Jaccard Similarity for High-Performance Distributed Genome Comparisons
Maciej Besta, Raghavendra Kanakagiri, Harun Mustafa +4
The Jaccard similarity index is an important measure of the overlap of two sets, widely used in machine learning, computational genomics, information retrieval, and many other area…
Scalable GW software for quasiparticle properties using OpenAtom
Minjung Kim, Subhasish Mandal, Eric Mikida +7
The GW method, which can describe accurately electronic excitations, is one of the most widely used ab initio electronic structure technique and allows the physics of both molecula…