Recent & Upcoming Talks
The growth of network-structured data across domains like neuroscience and cybersecurity demands scalable graph analytics, but complex tasks like subgraph isomorphism remain accessible only to high-performance computing (HPC) specialists. Arachne is …
A real-world challenge in data science is to develop interactive methods for quickly analyzing new and novel data sets that are potentially of massive scale. In this talk, Bader will discuss his development of graph algorithms in the context of …
The convergence of artificial intelligence and high-performance computing (HPC) is driving unprecedented advances in solving global grand challenges. From training foundation models and large language models (LLMs) that require thousands of GPUs, to …
Join us for an engaging discussion on how Generative AI is affecting the way we work, teach, learn, shop, communicate, etc. The goal of the symposium is to ideate research projects that will shape societal adaptation to the new technological …
In this talk, I explore the evolution and impact of large-scale graph analytics, from my pioneering work in Linux supercomputing to today's democratization of massive data science capabilities. The presentation highlights how the open-source Arachne …
The convergence of artificial intelligence and high-performance computing (HPC) is driving unprecedented advances in solving global grand challenges. From training foundation models and large language models (LLMs) that require thousands of GPUs, to …
The future of computing is being driven by the convergence of high-performance computing (HPC) and the growing potential of quantum technologies, combined with advanced frameworks for massive-scale data analytics. As organizations confront the …
Sigma Xi Distinguished Lecture: Solving Global Grand Challenges with High Performance Data Analytics
Emerging real-world graph problems include: detecting and preventing disease in human populations; revealing community structure in large social networks; protecting our elections from cyber-threats; and improving the resilience of the electric power …
A real-world challenge in data science is to develop interactive methods for quickly analyzing new and novel data sets that are potentially of massive scale. In this talk, Bader will discuss his development of graph algorithms in the context of …
A real-world challenge in data science is to develop interactive methods for quickly analyzing new and novel data sets that are potentially of massive scale. In this talk, Bader will discuss his development of graph algorithms in the context of …
This talk presents large-scale graph analytics for Connectomics using the open-source Arkouda/Arachne framework.
A real-world challenge in data science is to develop interactive methods for quickly analyzing new and novel data sets that are potentially of massive scale. In this talk, Bader will discuss his development of knowledge graph algorithms in the …
A real-world challenge in data science is to develop interactive methods for quickly analyzing new and novel data sets that are potentially of massive scale. In this talk, Bader will discuss his development of graph algorithms in the context of …
A real-world challenge in data science is to develop interactive methods for quickly analyzing new and novel data sets that are potentially of massive scale. In this talk, Bader will discuss his development of graph algorithms in the context of …
Emerging real-world graph problems include: detecting and preventing disease in human populations; revealing community structure in large social networks; protecting our elections from cyber-threats; and improving the resilience of the electric power …
A real-world challenge in data science is to develop interactive methods for quickly analyzing new and novel data sets that are potentially of massive scale. In this talk, Bader will discuss his development of graph algorithms in the context of …
A real-world challenge in data science is to develop interactive methods for quickly analyzing new and novel data sets that are potentially of massive scale. In this talk, Bader will discuss his development of graph algorithms in the context of …
Data science aims to solve grand global challenges such as: detecting and preventing disease in human populations; revealing community structure in large social networks; protecting our elections from cyber-threats, and improving the resilience of …
A real-world challenge in data science is to develop interactive methods for quickly analyzing new and novel data sets that are potentially of massive scale. In this talk, Bader will discuss his development of graph algorithms in the context of …
A real-world challenge in data science is to develop interactive methods for quickly analyzing new and novel data sets that are potentially of massive scale. In this talk, Bader will discuss his development of graph algorithms in the context of …
Emerging real-world graph problems include: detecting and preventing disease in human populations; revealing community structure in large social networks; protecting our elections from cyber-threats; and improving the resilience of the electric power …
Emerging real-world graph problems include: detecting and preventing disease in human populations; revealing community structure in large social networks; protecting our elections from cyber-threats; and improving the resilience of the electric power …
Emerging real-world graph problems include: detecting and preventing disease in human populations; revealing community structure in large social networks; protecting our elections from cyber-threats; and improving the resilience of the electric power …
In the last 5-10 years, Deep Neural Networks (DNNs) not only emerged as a new target class of applications for HPC researchers, but papers focusing on these workloads have started dominating HPC conferences. Rapidly increasing size of …
Data science aims to solve grand global challenges such as: detecting and preventing disease in human populations; revealing community structure in large social networks; protecting our elections from cyber-threats, and improving the resilience of …
Bader will present a keynote talk on massive-scale analytics for real-world applications.
Data science aims to solve grand global challenges such as: detecting and preventing disease in human populations; revealing community structure in large social networks; protecting our elections from cyber-threats, and improving the resilience of …
Data science aims to solve grand global challenges such as: detecting and preventing disease in human populations; revealing community structure in large social networks; protecting our elections from cyber-threats, and improving the resilience of …
Advances in computers, mobile devices, nanotechnologies and cyberinfrastructure are at the vanguard of profound transformations across society, in communications, healthcare, business and commerce, defense and even politics. Fueling this revolution …
With the advancements of high performance computing (HPC) and edge computing (EC) use cases are now arising for the need of combining the two for high performance edge computing (HPEC). New technologies in the world of HPEC would require not only the …
Data science aims to solve grand global challenges such as: detecting and preventing disease in human populations; revealing community structure in large social networks; protecting our elections from cyber-threats, and improving the resilience of …
Quantum computing is the processing of information that’s represented by special quantum states. By tapping into quantum phenomena like “superposition” and “entanglement,” these machines handle information in a fundamentally different way to …
Data science aims to solve grand global challenges such as: detecting and preventing disease in human populations; revealing community structure in large social networks; protecting our elections from cyber-threats, and improving the resilience of …
Data science aims to solve grand global challenges such as: detecting and preventing disease in human populations; revealing community structure in large social networks; protecting our elections from cyber-threats, and improving the resilience of …
Data science aims to solve grand global challenges such as: detecting and preventing disease in human populations; revealing community structure in large social networks; protecting our elections from cyber-threats, and improving the resilience of …
Supercomputing has become an essential tool for computational science and engineering and such real-world problems as weather prediction, jet design, molecular dynamics, and medical imaging. Such systems also provide unique capabilities for …
Supercomputing has become an essential tool for computational science and engineering and such real-world problems as weather prediction, jet design, molecular dynamics, and medical imaging. Such systems also provide unique capabilities for …
Data science aims to solve grand global challenges such as: detecting and preventing disease in human populations; revealing community structure in large social networks; protecting our elections from cyber-threats, and improving the resilience of …
Data science aims to solve grand global challenges such as: detecting and preventing disease in human populations; revealing community structure in large social networks; protecting our elections from cyber-threats, and improving the resilience of …
Data science aims to solve grand global challenges such as: detecting and preventing disease in human populations; revealing community structure in large social networks; protecting our elections from cyber-threats, and improving the resilience of …
A real-world challenge in data science is to develop interactive methods for quickly analyzing new and novel data sets that are potentially of massive scale. In this talk, we discuss our development of suffix array and graph algorithms in the context …
Data science aims to solve grand global challenges such as: detecting and preventing disease in human populations; revealing community structure in large social networks; protecting our elections from cyber-threats, and improving the resilience of …
Data science aims to solve grand global challenges such as: detecting and preventing disease in human populations; revealing community structure in large social networks; protecting our elections from cyber-threats, and improving the resilience of …
Data science aims to solve grand global challenges such as: detecting and preventing disease in human populations; revealing community structure in large social networks; protecting our elections from cyber-threats, and improving the resilience of …
As Data Science emerges as a new field, academic institutions around the world are following different routes toward establishing organizational units for the critical education and scholarship. There are several options for housing data science …
We'll introduce a new algorithm called anti-section transitive closure (ATC) for finding the transitive closure of a graph, including a new technique, called anti-sections, for finding reachable vertices. ATC works on both sparse and large networks. …
Data science aims to solve grand global challenges such as: detecting and preventing disease in human populations; revealing community structure in large social networks; and improving the resilience of the electric power grid. Unlike traditional …
Data science aims to solve grand global challenges such as: detecting and preventing disease in human populations; revealing community structure in large social networks; protecting our elections from cyber-threats, and improving the resilience of …
Data science aims to solve grand global challenges such as: detecting and preventing disease in human populations; revealing community structure in large social networks; protecting our elections from cyber-threats, and improving the resilience of …
Data science aims to solve grand global challenges such as: detecting and preventing disease in human populations; revealing community structure in large social networks; protecting our elections from cyber-threats, and improving the resilience of …
Data science aims to solve grand global challenges such as: detecting and preventing disease in human populations; revealing community structure in large social networks; protecting our elections from cyber-threats, and improving the resilience of …
Data science aims to solve grand global challenges such as: detecting and preventing disease in human populations; revealing community structure in large social networks; protecting our elections from cyber-threats, and improving the resilience of …
Data science aims to solve grand global challenges such as: detecting and preventing disease in human populations; revealing community structure in large social networks; protecting our elections from cyber-threats, and improving the resilience of …
Data science aims to solve grand global challenges such as: detecting and preventing disease in human populations; revealing community structure in large social networks; protecting our elections from cyber-threats, and improving the resilience of …
Data science aims to solve grand global challenges such as: detecting and preventing disease in human populations; revealing community structure in large social networks; and improving the resilience of the electric power grid. Unlike traditional …
Data science aims to solve grand global challenges such as: detecting and preventing disease in human populations; revealing community structure in large social networks; and improving the resilience of the electric power grid. Unlike traditional …
Deep learning has boosted the machine learning field at large and created significant increases in the performance of tasks including speech recognition, image classification, object detection, and recommendation. It has opened the door to complex …
Data science aims to solve grand global challenges such as: detecting and preventing disease in human populations; revealing community structure in large social networks; and improving the resilience of the electric power grid. Unlike traditional …
In this emerging era of analytics, nearly every government and industry sector envisions superiority by real-time decision making from torrential streams of data. Predictive analytics leverages the investments in new computer architectures …
The need to sift through massive datasets from applications in cybersecurity, social media, financial transactions, and sensor feeds, is driving the design of novel architectures. There are few programming models and generalized processor …
Government agencies and a variety of institutions from disparate areas (finance, healthcare, insurance, tele-communication, computing, etc.) have recognized the necessity of a new generation of systems and tools targeted at efficiently solving large …
Emerging real-world graph problems include: detecting and preventing disease in human populations; revealing community structure in large social networks; and improving the resilience of the electric power grid. Unlike traditional applications in …
Prof. **David Bader**, one of the nation’s leading experts in massive-scale graph analytics, presents a Neo4j case study on predictive analytics on a homeland security knowledge graph that connects disparate data from multiple sources such as …
Emerging real-world graph problems include: detecting and preventing disease in human populations; revealing community structure in large social networks; and improving the resilience of the electric power grid. Unlike traditional applications in …
Emerging real-world graph problems include: detecting and preventing disease in human populations; revealing community structure in large social networks; and improving the resilience of the electric power grid. Unlike traditional applications in …
Prof. David Bader, one of the nation’s leading experts in massive-scale graph analytics, presents a case study on predictive analytics on a homeland security knowledge graph that connects disparate data from multiple sources such as spreadsheets and …
Emerging real-world graph problems include: detecting community structure in large social networks; improving the resilience of the electric power grid; and detecting and preventing disease in human populations. Unlike traditional applications in …
Emerging real-world graph problems include: detecting community structure in large social networks; improving the resilience of the electric power grid; and detecting and preventing disease in human populations. Unlike traditional applications in …
Emerging real-world graph problems include: detecting community structure in large social networks; improving the resilience of the electric power grid; and detecting and preventing disease in human populations. Unlike traditional applications in …
Government agencies and a variety of institutions from disparate areas (finance, healthcare, insurance, tele-communication, computing, etc.) have recognized the necessity of a new generation of systems and tools targeted at efficiently solving large …
Emerging real-world graph problems include: detecting community structure in large social networks; improving the resilience of the electric power grid; and detecting and preventing disease in human populations. Unlike traditional applications in …
Emerging real-world graph problems include: detecting community structure in large social networks; improving the resilience of the electric power grid; and detecting and preventing disease in human populations. Unlike traditional applications in …
Emerging real-world graph problems include: detecting community structure in large social networks; improving the resilience of the electric power grid; and detecting and preventing disease in human populations. Unlike traditional applications in …
Emerging real-world graph problems include: detecting community structure in large social networks; improving the resilience of the electric power grid; and detecting and preventing disease in human populations. Unlike traditional applications in …
Emerging real-world graph problems include: detecting community structure in large social networks; improving the resilience of the electric power grid; and detecting and preventing disease in human populations. Unlike traditional applications in …
Emerging real-world graph problems include: detecting community structure in large social networks; improving the resilience of the electric power grid; and detecting and preventing disease in human populations. Unlike traditional applications in …
On July 29, 2015, President Obama issued an Executive Order establishing the National Strategic Computing Initiative (NSCI) to ensure the United States continues leading in this field over the coming decades. The White House National Strategic …
Emerging real-world graph problems include: detecting community structure in large social networks; improving the resilience of the electric power grid; and detecting and preventing disease in human populations. Unlike traditional applications in …
Emerging real-world graph problems include: detecting community structure in large social networks; improving the resilience of the electric power grid; and detecting and preventing disease in human populations. Unlike traditional applications in …
Emerging real-world graph problems include: detecting community structure in large social networks; improving the resilience of the electric power grid; and detecting and preventing disease in human populations. Unlike traditional applications in …
Emerging real-world graph problems include detecting community structure in large social networks, improving the resilience of the electric power grid, and detecting and preventing disease in human populations. Unlike traditional applications in …
The HPC Connection workshop aims to provide an international communication platform for High Performance Computing (HPC) users and providers.
Big Data and research computing go hand-in-hand, especially for research disciplines that operate at extreme scales. Current Big Data solutions can be improved by borrowing from HPC techniques. A panel of experts in bioinformatics, quantum chemistry, …
Emerging real-world graph problems include detecting community structure in large social networks, improving the resilience of the electric power grid, and detecting and preventing disease in human populations. Unlike traditional applications in …
Large-scale data analytics applications represent increasingly important workloads but most of today's supercomputers are ill suited to them. Backed by a steering committee of over 30 international HPC experts from academia, industry, and national …
Emerging real-world graph problems include: detecting community structure in large social networks; improving the resilience of the electric power grid; and detecting and preventing disease in human populations. Unlike traditional applications in …
Panel Discussion Abstract: There is a lot of excitement about “Big Data” which is at the intersection of the ongoing explosion in data (volumes, variety, and velocity at which it arrives and must be acted upon), the dramatic increase in …
Emerging real-world graph problems include: detecting community structure in large social networks; improving the resilience of the electric power grid; and detecting and preventing disease in human populations. Unlike traditional applications in …
Data intensive computing, popularly known as Big Data, has grown enormously in importance over the past 5 years. However, most data intensive computing is focused on conventional analytics: searching, aggregating and summarizing the data set. Graph …
Cyber security increases in complexity and network connectivity every day. Todays problems are no longer limited to malware using hash functions. Interesting problems, such as coordinated cyber events, involve hundreds of millions to billions of …
Data intensive applications represent increasingly important workloads but are ill suited for most of todays machines. The Graph500 has demonstrated the challenges of even simple analytics. Backed by a steering committee of over 30 international HPC …
Emerging real-world graph problems include detecting community structure in large social networks, improving the resilience of the electric power grid, and detecting and preventing disease in human populations. Unlike traditional applications in …
Emerging real-world graph problems include detecting community structure in large social networks, improving the resilience of the electric power grid, and detecting and preventing disease in human populations. Unlike traditional applications in …
Emerging real-world graph problems include detecting community structure in large social networks, improving the resilience of the electric power grid, and detecting and preventing disease in human populations. Unlike traditional applications in …
Emerging real-world graph problems include detecting community structure in large social networks, improving the resilience of the electric power grid, and detecting and preventing disease in human populations. Unlike traditional applications in …
Emerging real-world graph problems include detecting community structure in large social networks, improving the resilience of the electric power grid, and detecting and preventing disease in human populations. Unlike traditional applications in …
Emerging real-world graph problems include detecting community structure in large social networks, improving the resilience of the electric power grid, and detecting and preventing disease in human populations. Unlike traditional applications in …
Organized by the Deutsches Klimarechenzentrum GmbH (DKRZ), Max-Planck-Institut für Meteorologie (MPI-M) and Cray Inc.
Data intensive supercomputer applications are increasingly important HPC workloads, but are ill suited for platforms designed for 3D physics simulations. Current benchmarks and performance metrics do not provide useful information on the suitability …
Graph theoretic problems are representative of fundamental kernels in traditional and emerging computational sciences such as chemistry, biology, and medicine, as well as applications in national security. Yet they pose serious challenges for …
Emerging real-world graph problems include detecting community structure in large social networks, improving the resilience of the electric power grid, and detecting and preventing disease in human populations. Unlike traditional applications in …
Emerging real-world graph problems include detecting community structure in large social networks, improving the resilience of the electric power grid, and detecting and preventing disease in human populations. Unlike traditional applications in …
Emerging real-world graph problems include detecting community structure in large social networks, improving the resilience of the electric power grid, and detecting and preventing disease in human populations. Unlike traditional applications in …
Emerging real-world graph problems include detecting community structure in large social networks, improving the resilience of the electric power grid, and detecting and preventing disease in human populations. Unlike traditional applications in …
Emerging real-world graph problems include detecting community structure in large social networks, improving the resilience of the electric power grid, and detecting and preventing disease in human populations. Unlike traditional applications in …
Emerging real-world graph problems include detecting community structure in large social networks, improving the resilience of the electric power grid, and detecting and preventing disease in human populations. Unlike traditional applications in …
The STI Cell Broadband Engine (Cell BE) has shown the potential to provide outstanding performance, scalability, and flexibility in applications with high data parallelism, such as dense and sparse matrix operations, image processing, and encryption. …
Graph theoretic problems are representative of fundamental kernels in traditional and emerging computational sciences such as chemistry, biology, and medicine, as well as applications in national security. Yet they pose serious challenges for …
While we are still witnessing Moore's Law by the steady production of chips that mass billions of transistors, clearly we have reached plateaus on clock frequency, power, and single stream performance. This new era has caused a rethinking of …
Graph theoretic problems are representative of fundamental kernels in traditional and emerging computational sciences such as chemistry, biology, and medicine, as well as applications in national security. Yet they pose serious challenges for …
Graph theoretic problems are representative of fundamental kernels in traditional and emerging computational sciences such as chemistry, biology, and medicine, as well as applications in national security. Yet they pose serious challenges for …
Graph theoretic problems are representative of fundamental kernels in traditional and emerging computational sciences such as chemistry, biology, and medicine, as well as applications in national security. Yet they pose serious challenges for …
Computational science enables us to investigate phenomena where economics or constraints preclude experimentation, evaluate complex models and manage massive data volumes, model processes across interdisciplinary boundaries, and transform business …
Computational science enables us to investigate phenomena where economics or constraints preclude experimentation, evaluate complex models and manage massive data volumes, model processes across interdisciplinary boundaries, and transform business …
Graph theoretic problems are representative of fundamental kernels in traditional and emerging computational sciences such as chemistry, biology, and medicine, as well as applications in national security. Yet they pose serious challenges for …
Universitat Politècnica de Catalunya HPC Seminar: Petascale Computing for Large-Scale Graph Problems
Graph theoretic problems are representative of fundamental kernels in traditional and emerging computational sciences such as chemistry, biology, and medicine, as well as applications in national security. Yet they pose serious challenges for …
Graph theoretic problems are representative of fundamental kernels in traditional and emerging computational sciences such as chemistry, biology, and medicine, as well as applications in national security. Yet they pose serious challenges for …
Computational science enables us to investigate phenomena where economics or constraints preclude experimentation, evaluate complex models and manage massive data volumes, model processes across interdisciplinary boundaries, and transform business …
Computational science enables us to investigate phenomena where economics or constraints preclude experimentation, evaluate complex models and manage massive data volumes, model processes across interdisciplinary boundaries, and transform business …
Graph theoretic problems are representative of fundamental kernels in traditional and emerging computational sciences such as chemistry, biology, and medicine, as well as applications in national security. Yet they pose serious challenges for …
Graph theoretic problems are representative of fundamental kernels in traditional and emerging computational sciences such as chemistry, biology, and medicine, as well as applications in national security. Yet they pose serious challenges for …
Graph theoretic problems are representative of fundamental kernels in traditional and emerging computational sciences such as chemistry, biology, and medicine, as well as applications in national security. Yet they pose serious challenges for …
Graph theoretic problems are representative of fundamental kernels in traditional and emerging computational sciences such as chemistry, biology, and medicine, as well as applications in national security. Yet they pose serious challenges for …
Graph theoretic problems are representative of fundamental kernels in traditional and emerging computational sciences such as chemistry, biology, and medicine, as well as applications in national security. Yet they pose serious challenges for …
Graph theoretic problems are representative of fundamental kernels in traditional and emerging computational sciences such as chemistry, biology, and medicine, as well as applications in national security. Yet they pose serious challenges for …
Graph theoretic problems are representative of fundamental kernels in traditional and emerging computational sciences such as chemistry, biology, and medicine, as well as applications in national security. Yet they pose serious challenges for …
Phylogenies derived from gene order data may prove crucial in answering some fundamental questions in biomolecular evolution. Yet very few techniques are available for phylogenetic reconstruction based upon gene order and content, and these are (for …
Graph theoretic problems are representative of fundamental kernels in traditional and emerging computational sciences such as chemistry, biology, and medicine, as well as applications in national security. Yet they pose serious challenges for …
Phylogenies derived from gene order data may prove crucial in answering some fundamental questions in biomolecular evolution. Yet very few techniques are available for phylogenetic reconstruction based upon gene order and content, and these are (for …
Many large-scale optimization problems rely on graph theoretic solutions; yet high-performance computing has traditionally focused on regular applications with high degrees of locality. We describe our novel methodology for designing and implementing …
Phylogenies derived from gene order data may prove crucial in answering some fundamental questions in biomolecular evolution. Yet very few techniques are available for phylogenetic reconstruction based upon gene order and content, and these are (for …
Combinatorial problems such as those from graph theory pose serious challenges for parallel machines due to non-contiguous, concurrent accesses to global data structures with low degrees of locality. The hierarchical memory systems of symmetric …
Phylogenies derived from gene order data may prove crucial in answering some fundamental questions in biomolecular evolution. Yet very few techniques are available for phylogenetic reconstruction based upon gene order and content, and these are (for …
Phylogenies derived from gene order data may prove crucial in answering some fundamental questions in biomolecular evolution. Yet very few techniques are available for phylogenetic reconstruction based upon gene order and content, and these are (for …
Many large-scale optimization problems rely on graph theoretic solutions; yet high-performance computing has traditionally focused on regular applications with high degrees of locality. We describe our novel methodology for designing and implementing …
Phylogenies derived from gene order data may prove crucial in answering some fundamental questions in biomolecular evolution. Yet very few techniques are available for phylogenetic reconstruction based upon gene order and content, and these are (for …
Phylogenies derived from gene order data may prove crucial in answering some fundamental questions in biomolecular evolution. Yet very few techniques are available for phylogenetic reconstruction based upon gene order and content, and these are (for …
In this talk, we present a new framework for measuring the productivity of high-end systems, propose new performance metrics, and describe our novel methodology for designing irregular parallel algorithms for high-end systems (such as combinatorial …
Phylogenies derived from gene order data may prove crucial in answering some fundamental questions in biomolecular evolution. Yet very few techniques are available for phylogenetic reconstruction based upon gene order and content, and these are (for …
Phylogenies derived from gene order data may prove crucial in answering some fundamental questions in biomolecular evolution. Yet very few techniques are available for phylogenetic reconstruction based upon gene order and content, and these are (for …
Phylogenies derived from gene order data may prove crucial in answering some fundamental questions in biomolecular evolution. Yet very few techniques are available for phylogenetic reconstruction based upon gene order and content, and these are (for …
Phylogenies derived from gene order data may prove crucial in answering some fundamental questions in biomolecular evolution. Yet very few techniques are available for phylogenetic reconstruction based upon gene order and content, and these are (for …
Phylogenies derived from gene order data may prove crucial in answering some fundamental questions in biomolecular evolution. Yet very few techniques are available for phylogenetic reconstruction based upon gene order and content, and these are (for …
Phylogenies derived from gene order data may prove crucial in answering some fundamental questions in biomolecular evolution. Yet very few techniques are available for phylogenetic reconstruction based upon gene order and content, and these are (for …
Phylogenies derived from gene order data may prove crucial in answering some fundamental questions in biomolecular evolution. Yet very few techniques are available for phylogenetic reconstruction based upon gene order and content, and these are (for …
Phylogenies derived from gene order data may prove crucial in answering some fundamental questions in biomolecular evolution. Yet very few techniques are available for phylogenetic reconstruction based upon gene order and content, and these are (for …
Shared-memory architectures with uniform memory access come very close to the PRAM, the theoretical model of parallel computing, and stand in sharp contrast to the common cluster approach. While PRAM algorithms have been devised for a large variety …
Phylogenies derived from gene order data may prove crucial in answering some fundamental questions in biomolecular evolution. Yet very few techniques are available for phylogenetic reconstruction based upon gene order and content, and these are (for …
Shared-memory architectures with uniform memory access come very close to the PRAM, the theoretical model of parallel computing, and stand in sharp contrast to the common cluster approach. While PRAM algorithms have been devised for a large variety …
Shared-memory architectures with uniform memory access come very close to the PRAM, the theoretical model of parallel computing, and stand in sharp contrast to the common cluster approach. While PRAM algorithms have been devised for a large variety …
Shared-memory architectures with uniform memory access come very close to the PRAM, the theoretical model of parallel computing, and stand in sharp contrast to the common cluster approach. While PRAM algorithms have been devised for a large variety …
Shared-memory architectures with uniform memory access come very close to the PRAM, the theoretical model of parallel computing, and stand in sharp contrast to the common cluster approach. While PRAM algorithms have been devised for a large variety …
A fundamental problem in high-performance computing is to design high-level, architecture independent, algorithms that execute efficiently on general purpose parallel machines. We describe a methodology for developing high performance programs …
A fundamental problem in high-performance computing is to design high-level, architecture independent, algorithms that execute efficiently on general purpose parallel machines. We describe a methodology for developing high performance programs …
With the cost of commercial off-the-shelf (COTS) high performance interconnects falling and the respective performance of microprocessors increasing, workstation clusters have become an attractive computing platform offering potentially a superior …
A common statistical problem is that of finding the median element in a set of data. This paper presents an efficient randomized high-level parallel algorithms for finding the median given a set of elements distributed across a parallel machine. In …
With the cost of commercial off-the-shelf (COTS) high performance interconnects falling and the respective performance of microprocessors increasing, workstation clusters have become an attractive computing platform offering potentially a superior …
With the cost of commercial off-the-shelf (COTS) high performance interconnects falling and the respective performance of microprocessors increasing, workstation clusters have become an attractive computing platform offering potentially a superior …
With the cost of commercial off-the-shelf (COTS) high performance interconnects falling and the respective performance of microprocessors increasing, workstation clusters have become an attractive computing platform offering potentially a superior …
A fundamental challenge for parallel computing is to obtain high-level, architecture independet, algorithms which efficiently execute on general-purpose parallel machines. With the emergence of message passing standards such as MPI, it has become …
A fundamental challenge for parallel computing is to obtain high-level, architecture independet, algorithms which efficiently execute on general-purpose parallel machines. With the emergence of message passing standards such as MPI, it has become …
A fundamental challenge for parallel computing is to obtain high-level, architecture independet, algorithms which efficiently execute on general-purpose parallel machines. With the emergence of message passing standards such as MPI, it has become …
A fundamental challenge for parallel computing is to obtain high-level, architecture independent, algorithms which execute efficiently on general-purpose parallel machines. With the emergence of message passing standards such as MPI, it has become …
This talk presents efficient and portable implementations for several useful primitives in image processing algorithms, such as histogramming, noise estimation, and connected components. An image segmentation process which makes use of these …
