40 citations · 78 across the 14 of their papers we have counts for
16 papers
Ab-initio Quantum Transport with the GW Approximation, 42,240 Atoms, and Sustained Exascale Performance
Nicolas Vetsch, Alexander Maeder, Vincent Maillou +7
Designing nanoscale electronic devices such as the currently manufactured nanoribbon field-effect transistors (NRFETs) requires advanced modeling tools capturing all relevant quant…
BLaST: High Performance Inference and Pretraining using BLock Sparse Transformers
Patrik Okanovic, Sameer Deshmukh, Grzegorz Kwasniewski +8
The energy consumption of large-scale ML models is dominated by data movement, shuffling billions of parameters across memory hierarchies and data centers. Sparsification offers a…
High Performance Unstructured SpMM Computation Using Tensor Cores
Patrik Okanovic, Grzegorz Kwasniewski, Paolo Sylos Labini +3
High-performance sparse matrix-matrix (SpMM) multiplication is paramount for science and industry, as the ever-increasing sizes of data prohibit using dense data structures. Yet, e…
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…
Deinsum: Practically I/O Optimal Multilinear Algebra
Alexandros Nikolaos Ziogas, Grzegorz Kwasniewski, Tal Ben-Nun +2
Multilinear algebra kernel performance on modern massively-parallel systems is determined mainly by data movement. However, deriving data movement-optimal distributed schedules for…
Lifting C Semantics for Dataflow Optimization
Alexandru Calotoiu, Tal Ben-Nun, Grzegorz Kwasniewski +4
C is the lingua franca of programming and almost any device can be programmed using C. However, programming mod-ern heterogeneous architectures such as multi-core CPUs and GPUs req…
On the Parallel I/O Optimality of Linear Algebra Kernels: Near-Optimal Matrix Factorizations
Grzegorz Kwasniewski, Marko Kabić, Tal Ben-Nun +8
Matrix factorizations are among the most important building blocks of scientific computing. State-of-the-art libraries, however, are not communication-optimal, underutilizing curre…
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…
Pebbles, Graphs, and a Pinch of Combinatorics: Towards Tight I/O Lower Bounds for Statically Analyzable Programs
Grzegorz Kwasniewski, Tal Ben-Nun, Lukas Gianinazzi +5
Determining I/O lower bounds is a crucial step in obtaining communication-efficient parallel algorithms, both across the memory hierarchy and between processors. Current approaches…
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…
GraphMineSuite: Enabling High-Performance and Programmable Graph Mining Algorithms with Set Algebra
Maciej Besta, Zur Vonarburg-Shmaria, Yannick Schaffner +16
We propose GraphMineSuite (GMS): the first benchmarking suite for graph mining that facilitates evaluating and constructing high-performance graph mining algorithms. First, GMS com…
SeBS: A Serverless Benchmark Suite for Function-as-a-Service Computing
Marcin Copik, Grzegorz Kwasniewski, Maciej Besta +2
Function-as-a-Service (FaaS) is one of the most promising directions for the future of cloud services, and serverless functions have immediately become a new middleware for buildin…
On the Parallel I/O Optimality of Linear Algebra Kernels: Near-Optimal LU Factorization
Grzegorz Kwasniewski, Tal Ben-Nun, Alexandros Nikolaos Ziogas +3
Dense linear algebra kernels, such as linear solvers or tensor contractions, are fundamental components of many scientific computing applications. In this work, we present a novel…
Flexible Communication Avoiding Matrix Multiplication on FPGA with High-Level Synthesis
Johannes de Fine Licht, Grzegorz Kwasniewski, Torsten Hoefler
Data movement is the dominating factor affecting performance and energy in modern computing systems. Consequently, many algorithms have been developed to minimize the number of I/O…
A scalable weakly-synchronous algorithm for solving partial differential equations
Konduri Aditya, Tobias Gysi, Grzegorz Kwasniewski +3
Synchronization overheads pose a major challenge as applications advance towards extreme scales. In current large-scale algorithms, synchronization as well as data communication de…
Red-blue pebbling revisited: near optimal parallel matrix-matrix multiplication
Grzegorz Kwasniewski, Marko Kabić, Maciej Besta +3
We propose COSMA: a parallel matrix-matrix multiplication algorithm that is near communication-optimal for all combinations of matrix dimensions, processor counts, and memory sizes…