We recently outlined the vision of "Learning Everywhere" which captures the possibility and impact of how learning methods and traditional HPC methods can be coupled together. A primary driver of such coupling is the promise that Machine Learning (ML) will give major performance improvements for traditional HPC simulat…
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arXiv research
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31 results for “HPC”
Tackles the computational hardness of HPC detection, conjecturing equivalence to PC detection.
problem Computational hardness of hypergraphic planted clique detection.
method No specific method mentioned; focuses on conjecturing equivalence.
result Equivalence of computational hardness between HPC and PC detection.
Scalable3-BO tackles scalability issues in Bayesian optimization for big data and high dimensions.
problem Bayesian optimization scalability issues in big data and high dimensions.
method Sparse Gaussian process, random embedding, asynchronous parallelization.
result Scalable3-BO framework optimizes high-dimensional problems with 1 million data points and 10,000 dimensions.
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…
AI and HPC help screen millions of molecules for SARS-CoV-2 treatments.
problem Finding effective treatments for SARS-CoV-2.
method AI and HPC enable screening of large molecule datasets.
result Data release of 23 datasets with 4.2 billion molecules.
A new method for efficient optimization of expensive simulations on HPC.
problem Efficiently solving computationally expensive simulation-based optimization problems.
method Asynchronous parallel Bayesian optimization with budgeted computational resource.
result Improved efficiency and handling of constraints in optimization.
LightOn OPUs accelerate randomized numerical linear algebra, reducing computational costs.
problem Computational bottleneck in randomization step for large-scale linear algebra.
method Near constant-time linear random projections from LightOn OPUs.
result Significant acceleration of RandNLA algorithms with negligible precision loss.
Quantum algorithm speeds up pricing of financial derivatives.
problem Pricing autocallable options efficiently.
method Integration-based exponential amplitude loading technique.
result 50x reduction in circuit depth for payoff component.
We present a novel hybrid algorithm for Bayesian network structure learning, called Hybrid HPC (H2PC). It first reconstructs the skeleton of a Bayesian network and then performs a Bayesian-scoring greedy hill-climbing search to orient the edges. It is based on a subroutine called HPC, that combines ideas from increment…
Smartfluidnet accelerates Eulerian fluid simulation with neural networks.
problem Current neural network methods for Eulerian fluid simulation lack flexibility and generalization.
method Smartfluidnet automates model generation and dynamic switching to meet user requirements.
result Smartfluidnet achieves 1.46x and 590x speedup compared to state-of-the-art models, with better simulation quality.
Flexible framework improves communication efficiency across various systems.
problem Reducing communication between nodes in machine learning tasks.
method Adapts compression level to true gradient at each iteration, optimizing per-bit improvement.
result Automatic tuning strategies significantly increase communication efficiency.
Paper proposes counterfactual explanations for ML on multivariate time series data.
problem Lack of user trust and difficulty in debugging ML frameworks using multivariate time series data.
method Proposes a novel explainability technique for providing counterfactual explanations.
result Outperforms state-of-the-art explainability methods in metrics like faithfulness and robustness.
Deep learning, through the use of neural networks, has demonstrated remarkable ability to automate many routine tasks when presented with sufficient data for training. The neural network architecture (e.g. number of layers, types of layers, connections between layers, etc.) plays a critical role in determining what, if…
Power and thermal management are critical components of High-Performance-Computing (HPC) systems, due to their high power density and large total power consumption. The assessment of thermal dissipation by means of compact models directly from the thermal response of the final device enables more robust and precise the…
Paper explores limits of high-order clustering with planted structures.
problem Statistical and computational limits of high-order clustering with planted structures.
method Developed methods for detection and recovery of clusters, identified signal-to-noise ratio boundaries.
result Sharp boundaries of signal-to-noise ratio for statistical and computational feasibility.
The paper investigates overfitting in hyperparameter optimization.
problem Overfitting in hyperparameter optimization (overtuning).
method Formal definition, large-scale reanalysis of HPO benchmark data, analysis of factors affecting overtuning.
result Overtuning is more common than previously assumed, leading to worse generalization error in 10% of cases.
Stochastic variational inference (SVI) employs stochastic optimization to scale up Bayesian computation to massive data. Since SVI is at its core a stochastic gradient-based algorithm, horizontal parallelism can be harnessed to allow larger scale inference. We propose a lock-free parallel implementation for SVI which a…
Deep Learning algorithms have recently become the de-facto paradigm for various prediction problems, which include many privacy-preserving applications like online medical image analysis. Presumably, the privacy of data in a deep learning system is a serious concern. There have been several efforts to analyze and explo…
Machine learning models outperform traditional CAPM in forecasting financial asset prices.
problem Predicting and forecasting financial asset prices and returns.
method Comparison of modern Machine Learning algorithms with the Capital Asset Pricing Model (CAPM) on U.S. equities data.
result Implemented Machine Learning models significantly outperform the CAPM on out-of-sample test data.
Paper speeds up topological signal identification and cycle matching.
problem Efficiently identifying and matching topological signals across datasets.
method Cohomological approach to persistent homology computation.
result Significantly faster performance on large-scale datasets.
Running high-resolution physical models is computationally expensive and essential for many disciplines. Agriculture, transportation, and energy are sectors that depend on high-resolution weather models, which typically consume many hours of large High Performance Computing (HPC) systems to deliver timely results. Many…
Motivation: With the development of droplet based systems, massive single cell transcriptome data has become available, which enables analysis of cellular and molecular processes at single cell resolution and is instrumental to understanding many biological processes. While state-of-the-art clustering methods have been…
Gene expression profiles have been widely used to characterize patterns of cellular responses to diseases. As data becomes available, scalable learning toolkits become essential to processing large datasets using deep learning models to model complex biological processes. We present an autoencoder to capture nonlinear …
Hess-MC2: Sequential Monte Carlo Squared using Hessian Information and Second Order Proposalsstat.ML
Enhances SMC² with Hessian info for more efficient posterior approximation.
problem Improving accuracy and efficiency in Bayesian inference.
method Integrates second-order information (Hessian) into SMC²'s proposal distribution.
result Second-order proposals lead to more accurate posterior approximations and better step-size selection.
Most commonly used distributed machine learning systems are either synchronous or centralized asynchronous. Synchronous algorithms like AllReduce-SGD perform poorly in a heterogeneous environment, while asynchronous algorithms using a parameter server suffer from 1) communication bottleneck at parameter servers when wo…
Deep Generative Models that Solve PDEs: Distributed Computing for Training Large Data-Free Modelscs.LG
New framework trains large SciML models solving PDEs in reasonable time.
problem Training large SciML models solving PDEs is challenging and time-consuming.
method Data parallel distributed deep learning framework with optimized methods.
result Neural PDE solvers can be viably trained for practical applications.
Deep generative models have recently achieved impressive results for many real-world applications, successfully generating high-resolution and diverse samples from complex datasets. Due to this improvement, fake digital contents have proliferated growing concern and spreading distrust in image content, leading to an ur…
Exact Gaussian Processes for Massive Datasets via Non-Stationary Sparsity-Discovering Kernelsstat.ML
Exact Gaussian Processes for massive datasets using non-stationary sparsity-discovering kernels.
problem High computational and storage costs for exact GPs in large datasets.
method Develop non-stationary kernels that allow the GP to discover sparse structure naturally.
result Exact Gaussian Processes scalable to over 5 million data points.
Software and hardware co-design and optimization of HPC systems has become intolerably complex, ad-hoc, time consuming and error prone due to enormous number of available design and optimization choices, complex interactions between all software and hardware components, and multiple strict requirements placed on perfor…
Bayesian Neural Networks detect gravitational wave events with high accuracy and real-time potential.
problem Detecting and identifying the full duration of compact binary coalescence events in gravitational wave data.
method Integrating Bayesian approach into a CLDNN classifier that combines CNN and LSTM for event detection and uncertainty estimation.
result Successfully detected all seven BBH events in LIGO Livingston O2 data with high accuracy.
Adaptive Periodic Averaging: A Practical Approach to Reducing Communication in Distributed Learningcs.LG
ADPSGD optimizes SGD convergence by varying averaging periods based on model variance.
problem High communication costs in parallel SGD.
method Varying averaging period based on model variance.
result ADPSGD achieves better convergence and smaller communication compared to CPSGD.