GPU accelerates Bayesian inference of RSV model up to 17x faster.
problem Bayesian inference of realized stochastic volatility model.
method Hybrid Monte Carlo (HMC) algorithm parallelized on GPU (GTX 760) and CPU (Intel i7-4770 3.4GHz).
result GPU can achieve up to 17 times faster computation compared to CPU.
A parallel Fortran framework for neural networks and deep learning.
problem Developing efficient parallel Fortran for neural networks and deep learning.
method Simple interface, activation functions, stochastic gradient descent, Fortran 2018 collective subroutines, parallelism with derived types and collective operations.
result Ease of use and computational performance similar to existing machine learning frameworks, suitable for production.
t-SNE-CUDA accelerates visualization of large datasets on GPUs.
problem Efficiently visualizing high-dimensional datasets and models.
method GPU-accelerated t-SNE implementation.
result Significant speedups (50-700x) on large datasets.
Neural machine translation used to convert CUDA to OpenCL.
problem Translating CUDA to OpenCL programs.
method Training input set generation, pre/post processing, case study.
result Improved accuracy in translating CUDA to OpenCL.
CUDA optimized neural network predicts HbA1c from joint mobility and anthropometrics.
problem Early detection and accurate diagnosis of diabetes.
method Parallelized neural network using CUDA and C++ on Nvidia GPUs.
result Achieved high accuracy (95.65% on training, 86.67% on testing for males; 97.73% on training, 66.67% on testing for females).
ParaMonte simplifies Monte Carlo simulations for various scientific fields.
problem Efficiently performing Monte Carlo simulations for complex models.
method Unified, high-performance, parallelized library for C, C++, Fortran.
result Automates and streamlines Monte Carlo sampling for arbitrary-dimensional functions.
CuLE accelerates reinforcement learning on GPUs.
problem Limited CPU-GPU communication bandwidth in Atari Learning Environment.
method CUDA port of ALE, leveraging GPU parallelization and direct frame rendering.
result Up to 155M frames per hour on a single GPU, accelerating reinforcement learning.
A quantum walk-based method for generating precise probability distributions efficiently.
problem Generating high-precision probability distributions for various applications.
method Integrates variational quantum circuits with split-step quantum walks to dynamically tune coin parameters and evolve quantum states.
result Achieves high simulation fidelity and reduces computational overhead compared to conventional methods.
ConCuR generates high-quality CUDA kernels with concise reasoning traces.
problem Scarce high-quality data for kernel generation.
method Developed a pipeline to generate and curate high-quality CUDA kernels with reasoning traces.
result Our model achieves significant improvements in KernelBench setup.
PyTorch Geometric accelerates deep learning on graphs.
problem Efficient deep learning on graph data.
method Library for graph data, including methods and efficient mini-batch handling.
result High data throughput achieved through sparse GPU acceleration.
A new GP inference method using simplices for high-dimensional data.
problem Scalable Gaussian Processes in high dimensions.
method Developed a Simplex-GP method using a sparse simplicial grid to accelerate MVMs.
result Significantly faster GP inference in high dimensions compared to SKI.
MDFS selects important variables considering variable interactions, improving over simple filtering.
problem Discarding variable interactions leads to loss of relevant variables.
method MultiDimensional Feature Selection (MDFS) using information theory and CUDA C.
result Multidimensional analysis provides more reliable rankings of variable importance.
New algorithms cluster high-dimensional polygonal curves efficiently.
problem Clustering high-dimensional polygonal curves with many vertices.
method Johnson-Lindenstrauss projection for polygonal curves, subsampling, probabilistic reduction of dependency on vertices.
result Achieves sublinear dependency on the number of input curves.
CUDA CTDR tackles unsupervised domain adaptation without domain alignment.
problem Lack of direct methods for unlabeled target domain classification.
method Jointly learns CTDR on source and target distributions using contradistinguish loss and supervised loss.
result CUDA CTDR achieves state-of-the-art results on various domain adaptation datasets.
Proposes Learnergy, a Python framework for energy-based machine learning.
problem Lack of research and implementations around energy-based systems.
method Built upon PyTorch, Learnergy provides a more friendly environment and faster prototyping.
result Learnergy speeds up computational time using CUDA.
Improved model error correction online with neural networks in 4D-Var.
problem Reconstructing dynamics of imperfectly observed physical models.
method Weak-constraint 4D-Var framework with online neural network training.
result Online model error correction yields more accurate results than offline.
Fast-vollib offers high-performance option pricing and IV computation.
problem Efficiently pricing and computing implied volatility for financial models.
method Open-source Python library with PyTorch, JAX, and CUDA backends, implementing Halley and LBR algorithms.
result High-performance option pricing and IV computation with vectorized implementations.
Automates GPU kernel optimization for diverse applications.
problem Lack of systematic evaluation for multi-scenario GPU kernel optimization.
method Introduces MSKernelBench and CUDAMaster for automated optimization.
result Significant speedups across various operators, outperforming existing tools.
Accelerates Birkhoff projection for manifold-constrained hyper-connections with high accuracy and speed.
problem Inaccurate and slow Birkhoff projection in mHC implementations.
method Dual formulation, Newton's method, implicit differentiation, warp-level CUDA kernel.
result Substantial speedups and accuracy improvements in doubly stochastic projections.
KineticSim accelerates financial market simulations 3406x over CPU.
problem Simulating financial markets at scale with multi-agent models is bottlenecked by sequential processing and GPU kernel overhead.
method Formalized and implemented a reusable parallel design pattern for iterative multi-agent reductions in thread-block shared memory.
result Achieved a peak throughput of over 54.7 billion agent-events per second, delivering 3406x speedup over CPU.
KineticSim: A lightweight, high-performance execution engine for real-time market simulators
problem Simulating financial markets at scale with multi-agent models
method Reusable parallel design pattern: persistent, state-carrying clearing for iterative multi-agent reductions
result Reduces per-step critical-path depth from Theta(L+A) to Theta(log L + ceil(A/L))
Two Python frameworks, Blocks and Fuel, for deep learning.
problem Training complex neural networks on large datasets.
method Blocks is based on Theano, providing parametrized operations and utilities. Fuel provides a standard dataset format.
result Facilitates efficient training and manipulation of large datasets.
New software package for scalable DPMM inference on large datasets.
problem Scalability and practical adoption of Dirichlet Process Mixture Models.
method Efficient distributed sampling-based inference on CPUs and GPUs.
result Significant speedups and fitting of larger datasets.
SimTensor generates synthetic tensor data for research.
problem Reproducible research on tensor factorization algorithms.
method Multi-platform software for generating artificial tensor data with various configurations.
result Generates temporal tensors with periodic waves, seasonal effects, and streaming structure.
High performance computing (HPC) is a very attractive and relatively new area of research, which gives promising results in many applications. In this paper HPC is used for pricing of American options. Although the American options are very significant in computational finance; their valuation is very challenging, espe…
JAXFit speeds up curve fitting on GPUs.
problem Nonlinear least squares curve fitting problems.
method Trust region method on GPU with automatic differentiation.
result Significantly faster than CPU and other GPU libraries.
Random forests handle categorical predictors natively but overlook 'absent levels' can bias models.
problem Bias in decision tree models due to 'absent levels' problem.
method Examined with Leo Breiman and Adele Cutler's random forests FORTRAN code and the randomForest R package.
result Simple heuristics can help mitigate the effects of the absent levels problem.
DiffTaichi enables fast, differentiable physical simulations with shorter code.
problem Building efficient differentiable physical simulators.
method Differentiable programming language (DiffTaichi) that generates gradients using source code transformations and a light-weight tape.
result Differentiable physical simulators written in DiffTaichi are faster and more concise than existing methods.
GraphGP: Scalable Gaussian Processes with Vecchia's Approximation
problem Naive Gaussian Process computation limits practical use
method GPU algorithm for Vecchia's approximation
result Linear time and memory requirements for nearly a billion parameters
This paper improves SVD for recommender systems using block-based matrix factorization.
problem Scalability and performance issues in recommender systems.
method Block-based Singular Value Decomposition (BMF) for matrix factorization.
result BMF paired with SVD enhances performance and scalability.
Deep-MacroFin uses neural networks to solve complex economic models efficiently.
problem Solving high-dimensional partial differential equations in continuous time economics.
method Leverages deep learning, specifically Multi-Layer Perceptrons and Kolmogorov-Arnold Networks, optimized with HJB equations.
result Offers a more efficient solution (5imes less memory, 40imes fewer FLOPs) for 50D economic models. The pricing of American style and multiple exercise options is a very challenging problem in mathematical finance. One usually employs a Least-Square Monte Carlo approach (Longstaff-Schwartz method) for the evaluation of conditional expectations which arise in the Backward Dynamic Programming principle for such optimal…
Quasi-Monte Carlo speeds up option Greeks calculation on GPUs.
problem Efficiently calculating option Greeks for risk management.
method Quasi-Monte Carlo (QMC) combined with GPU acceleration for pathwise sensitivity calculation.
result Increased computational speed and efficiency in estimating option Greeks.
ParaDRAM automates parallel MCMC simulations across languages.
problem Efficiently sampling complex mathematical functions.
method Automated, parallel, high-performance MCMC with adaptive rejection.
result Significant memory reduction for complex simulations.
New GPU-based algorithm for fast optimal transport on brain tractograms.
problem Efficiently comparing and transferring labels in brain tractograms.
method Multiscale algorithm using Sinkhorn divergences on GPU.
result Smooth assignments for label transfer in tractograms.
SPFlow simplifies SPN-based probabilistic learning with a Python library.
problem Creating and manipulating deep probabilistic models efficiently.
method Provides a Python library with DSL for SPN creation, efficient inference routines, and structure learning.
result SPFlow enables quick and efficient probabilistic inference and learning for SPNs.
The paper develops a new simulation technique for estimating conditional expectations in financial models.
problem Estimating conditional expectations in financial models with expensive simulation of endogenous variables.
method Introduces a hierarchical simulation scheme with oversimplified defaults to address variance issues.
result The hierarchical simulation technique significantly improves the success of neural net regression for conditional expectation estimation.
A new meta-learning method improves deep neural net training efficiency.
problem Efficient training of complex deep neural networks with long training processes.
method Meta-learning with Hessian-Free (MLHF) approach based on Hessian-Free optimization.
result MLHF shows good and continuous training performance in deep convolution neural nets.
Advocates for user-friendly RL problem descriptions to improve usability and generalization.
problem Usability and generalization challenges in RL for non-engineers.
method Development of user-friendly description languages for RL problems.
result Improved ability of RL algorithms to generalize to new problems.
Performance-aware channel pruning improves CNN on embedded GPUs.
problem Inefficient channel pruning on embedded GPUs leads to performance slowdowns.
method Evaluate higher-level libraries that analyze input characteristics for optimized code generation.
result Performance-aware pruning can achieve significant performance speedups, up to 10x.
Deep CNNs segment heart substructures from MRI and CT scans.
problem Accurate segmentation of heart substructures from radiology scans.
method Multi-planar deep convolutional neural networks (CNNs) with adaptive fusion strategy.
result Precision and Dice Index of 0.93 and 0.90 for CT, and 0.87 and 0.85 for MRI.
This article presents differential equations and solution methods for the functions of the form Q(x)=F−1(G(x)), where F and G are cumulative distribution functions. Such functions allow the direct recycling of Monte Carlo samples from one distribution into samples from another. The method may be developed an…