Zobrazeno 1 - 10
of 255
pro vyhledávání: '"YELICK, KATHERINE"'
Publikováno v:
Proceedings of the 38th ACM International Conference on Supercomputing (ICS 2024) 225-235
Sparse matrix multiplication is an important kernel for large-scale graph processing and other data-intensive applications. In this paper, we implement various asynchronous, RDMA-based sparse times dense (SpMM) and sparse times sparse (SpGEMM) algori
Externí odkaz:
http://arxiv.org/abs/2311.18141
Graph Neural Networks (GNNs) offer a compact and computationally efficient way to learn embeddings and classifications on graph data. GNN models are frequently large, making distributed minibatch training necessary. The primary contribution of this p
Externí odkaz:
http://arxiv.org/abs/2311.02909
Autor:
Selvitopi, Oguz, Ekanayake, Saliya, Guidi, Giulia, Awan, Muaaz G., Pavlopoulos, Georgios A., Azad, Ariful, Kyrpides, Nikos, Oliker, Leonid, Yelick, Katherine, Buluç, Aydın
Publikováno v:
SC'22: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, 2022
Similarity search is one of the most fundamental computations that are regularly performed on ever-increasing protein datasets. Scalability is of paramount importance for uncovering novel phenomena that occur at very large scales. We unleash the powe
Externí odkaz:
http://arxiv.org/abs/2303.01845
Filters approximately store a set of items while trading off accuracy for space-efficiency and can address the limited memory on accelerators, such as GPUs. However, there is a lack of high-performance and feature-rich GPU filters as most advancement
Externí odkaz:
http://arxiv.org/abs/2212.09005
Autor:
Guidi, Giulia, Raulet, Gabriel, Rokhsar, Daniel, Oliker, Leonid, Yelick, Katherine, Buluc, Aydin
Publikováno v:
ICPP22, August 29-September 1, 2022, Bordeaux, France
De novo genome assembly, i.e., rebuilding the sequence of an unknown genome from redundant and erroneous short sequences, is a key but computationally intensive step in many genomics pipelines. The exponential growth of genomic data is increasing the
Externí odkaz:
http://arxiv.org/abs/2207.04350
Autor:
Chen, Yuxin, Brock, Benjamin, Porumbescu, Serban, Buluç, Aydın, Yelick, Katherine, Owens, John D.
We present Atos, a task-parallel GPU dynamic scheduling framework that is especially suited to dynamic irregular applications. Compared to the dominant Bulk Synchronous Parallel (BSP) frameworks, Atos exposes additional concurrency by supporting task
Externí odkaz:
http://arxiv.org/abs/2112.00132
We present a quantum synthesis algorithm designed to produce short circuits and to scale well in practice. The main contribution is a novel representation of circuits able to encode placement and topology using generic "gates", which allows the QFAST
Externí odkaz:
http://arxiv.org/abs/2103.07093
Publikováno v:
Companion of the 2021 ACM/SPEC International Conference on Performance Engineering (ICPE21 Companion)
Can cloud computing infrastructures provide HPC-competitive performance for scientific applications broadly? Despite prolific related literature, this question remains open. Answers are crucial for designing future systems and democratizing high-perf
Externí odkaz:
http://arxiv.org/abs/2011.00656
Understanding protein structure-function relationships is a key challenge in computational biology, with applications across the biotechnology and pharmaceutical industries. While it is known that protein structure directly impacts protein function,
Externí odkaz:
http://arxiv.org/abs/2010.16027
Autor:
Guidi, Giulia, Selvitopi, Oguz, Ellis, Marquita, Oliker, Leonid, Yelick, Katherine, Buluc, Aydin
One of the most computationally intensive tasks in computational biology is de novo genome assembly, the decoding of the sequence of an unknown genome from redundant and erroneous short sequences. A common assembly paradigm identifies overlapping seq
Externí odkaz:
http://arxiv.org/abs/2010.10055