Search results for "DUP"

showing 10 items of 499 documents

Sorafenib maintenance after allogeneic hematopoietic stem cell transplantation for acute myeloid leukemia with FLT3-internal tandem duplication mutat…

2020

PURPOSE Despite undergoing allogeneic hematopoietic stem cell transplantation (HCT), patients with acute myeloid leukemia (AML) with internal tandem duplication mutation in the FMS-like tyrosine kinase 3 gene ( FLT3-ITD) have a poor prognosis, frequently relapse, and die as a result of AML. It is currently unknown whether a maintenance therapy using FLT3 inhibitors, such as the multitargeted tyrosine kinase inhibitor sorafenib, improves outcome after HCT. PATIENTS AND METHODS In a randomized, placebo-controlled, double-blind phase II trial (SORMAIN; German Clinical Trials Register: DRKS00000591), 83 adult patients with FLT3-ITD–positive AML in complete hematologic remission after HCT were r…

SorafenibFLT3 Internal Tandem DuplicationCancer ResearchMyeloidbusiness.industrymedicine.medical_treatmentMyeloid leukemiaHematopoietic stem cell transplantationmedicine.diseaseTransplantationLeukemiamedicine.anatomical_structureOncologyhemic and lymphatic diseasesmedicineCancer researchNeoplasmbusinessmedicine.drug
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Reconstruction of Low Energy Neutrino Events with GPUs at IceCube

2020

IceCube is a cubic kilometer neutrino observatory located at the South Pole that produces massive amounts of data by measuring individual Cherenkov photons from neutrino interaction events in the energy range from few GeV to several PeV. The actual reconstruction of neutrino events in the GeV range is computationally challenging due to the scarcity of data produced by single events. This can lead to run times of several weeks for the state-of-the-art reconstruction method – Pegleg – on CPUs for typical workloads of many ten-thousand events. We propose a GPU version of Pegleg that probes the likelihood space with several hypotheses in parallel while adapting the amount of parallel sampled hy…

Speedup010308 nuclear & particles physicsComputer scienceAstrophysics::High Energy Astrophysical PhenomenaComputation01 natural sciencesComputational scienceTitan (supercomputer)Observatory0103 physical sciencesRange (statistics)Neutrino010306 general physicsNeutrino oscillationCherenkov radiation
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The Dynamical Kernel Scheduler - Part 1

2015

Emerging processor architectures such as GPUs and Intel MICs provide a huge performance potential for high performance computing. However developing software using these hardware accelerators introduces additional challenges for the developer such as exposing additional parallelism, dealing with different hardware designs and using multiple development frameworks in order to use devices from different vendors. The Dynamic Kernel Scheduler (DKS) is being developed in order to provide a software layer between host application and different hardware accelerators. DKS handles the communication between the host and device, schedules task execution, and provides a library of built-in algorithms. …

Speedup010308 nuclear & particles physicsComputer sciencebusiness.industryFast Fourier transformGeneral Physics and AstronomyFOS: Physical sciencesParallel computingComputational Physics (physics.comp-ph)Supercomputer01 natural sciencesCUDASoftwareKernel (image processing)Hardware and Architecture0103 physical sciencesHardware acceleration010306 general physicsbusinessPhysics - Computational PhysicsXeon Phi
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Optimization of Reactive Force Field Simulation: Refactor, Parallelization, and Vectorization for Interactions

2022

Molecular dynamics (MD) simulations are playing an increasingly important role in many areas ranging from chemical materials to biological molecules. With the continuing development of MD models, the potentials are getting larger and more complex. In this article, we focus on the reactive force field (ReaxFF) potential from LAMMPS to optimize the computation of interactions. We present our efforts on refactoring for neighbor list building, bond order computation, as well as valence angles and torsion angles computation. After redesigning these kernels, we develop a vectorized implementation for non-bonded interactions, which is nearly $100 \times$ 100 × faster than the management processing…

SpeedupComputational Theory and MathematicsXeonHardware and ArchitectureComputer scienceComputationSignal ProcessingVectorization (mathematics)Node (circuits)Parallel computingSupercomputerForce field (chemistry)Sunway TaihuLightIEEE Transactions on Parallel and Distributed Systems
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Reducing complexity in H.264/AVC motion estimation by using a GPU

2011

H.264/AVC applies a complex mode decision technique that has high computational complexity in order to reduce the temporal redundancies of video sequences. Several algorithms have been proposed in the literature in recent years with the aim of accelerating this part of the encoding process. Recently, with the emergence of many-core processors or accelerators, a new approach can be adopted for reducing the complexity of the H.264/AVC encoding algorithm. This paper focuses on reducing the inter prediction complexity adopted in H.264/AVC and proposes a GPU-based implementation using CUDA. Experimental results show that the proposed approach reduces the complexity by as much as 99% (100x of spe…

SpeedupComputational complexity theoryComputer science020206 networking & telecommunicationsData_CODINGANDINFORMATIONTHEORY02 engineering and technologyParallel computingCUDAAlgorithmic efficiency0202 electrical engineering electronic engineering information engineeringWorst-case complexity020201 artificial intelligence & image processingContext-adaptive binary arithmetic codingData compressionContext-adaptive variable-length coding
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CUDA-enabled Sparse Matrix–Vector Multiplication on GPUs using atomic operations

2013

We propose the Sliced Coordinate Format (SCOO) for Sparse Matrix-Vector Multiplication on GPUs.An associated CUDA implementation which takes advantage of atomic operations is presented.We propose partitioning methods to transform a given sparse matrix into SCOO format.An efficient Dual-GPU implementation which overlaps computation and communication is described.Extensive performance comparisons of SCOO compared to other formats on GPUs and CPUs are provided. Existing formats for Sparse Matrix-Vector Multiplication (SpMV) on the GPU are outperforming their corresponding implementations on multi-core CPUs. In this paper, we present a new format called Sliced COO (SCOO) and an efficient CUDA i…

SpeedupComputer Networks and CommunicationsComputer scienceSparse matrix-vector multiplicationParallel computingComputer Graphics and Computer-Aided DesignTheoretical Computer ScienceMatrix (mathematics)CUDAArtificial IntelligenceHardware and ArchitectureBenchmark (computing)MultiplicationGeneral-purpose computing on graphics processing unitsSoftwareSparse matrixParallel Computing
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Finding near-perfect parameters for hardware and code optimizations with automatic multi-objective design space explorations

2012

Summary In the design process of computer systems or processor architectures, typically many different parameters are exposed to configure, tune, and optimize every component of a system. For evaluations and before production, it is desirable to know the best setting for all parameters. Processing speed is no longer the only objective that needs to be optimized; power consumption, area, and so on have become very important. Thus, the best configurations have to be found in respect to multiple objectives. In this article, we use a multi-objective design space exploration tool called Framework for Automatic Design Space Exploration (FADSE) to automatically find near-optimal configurations in …

SpeedupComputer Networks and CommunicationsDesign space explorationComputer sciencebusiness.industryParallel computingProgram optimizationMulti-objective optimizationComputer Science ApplicationsTheoretical Computer ScienceMicroarchitectureComputational Theory and MathematicsScalabilityCode (cryptography)Engineering design processbusinessSoftwareComputer hardwareConcurrency and Computation: Practice and Experience
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CliffoSor: A Parallel Embedded Architecture for Geometric Algebra and Computer Graphics

2006

Geometric object representation and their transformations are the two key aspects in computer graphics applications. Traditionally, compute-intensive matrix calculations are involved to model and render 3D scenery. Geometric algebra (a.k.a. Clifford algebra) is gaining growing attention for its natural way to model geometric facts coupled with its being a powerful analytical tool for symbolic calculations. In this paper, the architecture of CliffoSor (Clifford Processor) is introduced. ClifforSor is an embedded parallel coprocessing core that offers direct hardware support to Clifford algebra operators. A prototype implementation on an FPGA board is detailed. Initial test results show more …

SpeedupComputer scienceClifford algebraSolid modelingParallel computingComputational geometryApplication softwarecomputer.software_genreComputational scienceComputer graphicsGeometric algebraComputingMethodologies_SYMBOLICANDALGEBRAICMANIPULATIONRepresentation (mathematics)computer
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Circuits and excitations to enable Brownian token-based computing with skyrmions

2021

Brownian computing exploits thermal motion of discrete signal carriers (tokens) for computations. In this paper we address two major challenges that hinder competitive realizations of circuits and application of Brownian token-based computing in actual devices for instance based on magnetic skyrmions. To overcome the problem that crossings generate for the fabrication of circuits, we design a crossing-free layout for a composite half-adder module. This layout greatly simplifies experimental implementations as wire crossings are effectively avoided. Additionally, our design is shorter to speed up computations compared to conventional designs. To address the key issue of slow computation base…

SpeedupCondensed Matter - Mesoscale and Nanoscale PhysicsPhysics and Astronomy (miscellaneous)Computer science530 PhysicsComputationFOS: Physical sciencesTopologySecurity token530 PhysikPower (physics)Discrete-time signalMesoscale and Nanoscale Physics (cond-mat.mes-hall)TorqueBrownian motionElectronic circuit
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First Experiences on an Accurate SPH Method on GPUs

2017

It is well known that the standard formulation of the Smoothed Particle Hydrodynamics is usually poor when scattered data distribution is considered or when the approximation near the boundary occurs. Moreover, the method is computational demanding when a high number of data sites and evaluation points are employed. In this paper an enhanced version of the method is proposed improving the accuracy and the efficiency by using a HPC environment. Our implementation exploits the processing power of GPUs for the basic computational kernel resolution. The performance gain demonstrates the method to be accurate and suitable to deal with large sets of data.

SpeedupExploitGPUsComputer scienceComputer Networks and CommunicationsGPUSmoothed Particle Hydrodynamics method010103 numerical & computational mathematics01 natural sciencesComputational scienceSmoothed-particle hydrodynamicsInstruction setSettore MAT/08 - Analisi NumericaArtificial IntelligenceAccuracy; Approximation; GPUs; Kernel function; Smoothed particle hydrodynamics method; Speed-Up; Artificial Intelligence; Computer Networks and Communications; 1707; Signal Processing0101 mathematicsApproximationAccuracy1707Random access memoryLinear systemKernel functionSpeed-Up010101 applied mathematicsKernel (statistics)Signal Processing
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