As machine learning algorithms enter applications in industrial settings, there is increased interest in controlling their cpu-time during testing. The cpu-time consists of the running time of the algorithm and the extraction time of the features. The latter can vary drastically when the feature set is diverse. In this…
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48 results for “CPU time”
Massively parallel architectures such as the GPU are becoming increasingly important due to the recent proliferation of data. In this paper, we propose a key class of hybrid parallel graphlet algorithms that leverages multiple CPUs and GPUs simultaneously for computing k-vertex induced subgraph statistics (called graph…
Asymmetry PRISM outperforms CPU and GPU solvers for institutional rebalancing.
problem Institutional rebalancing with deadline constraints
method Asymmetry PRISM
result Asymmetry PRISM-CPU is 4.5x to 24.1x faster than the fastest completed reference row in the same lane.
DLBricks automates DL benchmarking on CPUs, reducing effort and time.
problem Lack of representative and up-to-date DL benchmarks on CPUs.
method Decomposes DL models into runnable networks, leveraging layer repetition and auto-generation.
result Accurately estimates DL model performance and speeds up benchmarking time.
We present Caffe con Troll (CcT), a fully compatible end-to-end version of the popular framework Caffe with rebuilt internals. We built CcT to examine the performance characteristics of training and deploying general-purpose convolutional neural networks across different hardware architectures. We find that, by employi…
pySigLib speeds up signature-based computations on CPUs and GPUs.
problem Efficient signature-based computations on large datasets and long sequences.
method Optimised Python library for CPU and GPU, novel differentiation scheme.
result Accurate gradients at a fraction of the runtime of existing libraries.
This study uses LSTM and SARIMA models to forecast CPU usage in cloud computing.
problem Predicting future CPU usage in cloud computing centers.
method Used LSTM and SARIMA models to forecast CPU usage over short and long time scales.
result SARIMA model performed better for long-term predictions but LSTM was more robust.
New hybrid model reduces MILP solver time by up to 26%.
problem Improving CPU-based MILP solver efficiency.
method Combines GNN and MLP for efficient CPU-based branching.
result Up to 26% reduction in solver running time compared to state-of-the-art methods.
The realized stochastic volatility (RSV) model that utilizes the realized volatility as additional information has been proposed to infer volatility of financial time series. We consider the Bayesian inference of the RSV model by the Hybrid Monte Carlo (HMC) algorithm. The HMC algorithm can be parallelized and thus per…
GraphVite efficiently trains node embeddings for large networks.
problem Scaling node embedding algorithms to handle large networks (tens of millions of nodes).
method CPU-GPU hybrid system with parallel edge sampling and negative sampling.
result GraphVite achieves high performance (50x faster) for large networks.
Stochastic simulation techniques employed for the analysis of portfolios of insurance/reinsurance risk, often referred to as `Aggregate Risk Analysis', can benefit from exploiting state-of-the-art high-performance computing platforms. In this paper, parallel methods to speed-up aggregate risk analysis for supporting re…
LoRA fine-tuning on CPUs without GPUs achieves comparable performance to GPU-based methods.
problem Limited computational resources for fine-tuning LLMs.
method Developed a CPU-efficient method to learn meta-operators for LoRA weights.
result LoRA adapters trained on CPUs outperform base models on downstream tasks.
The paper optimizes RF training by improving tree building algorithms and CPU optimizations.
problem Improving the training performance of Random Forest models on CPU architectures.
method Investigated and improved tree building algorithms (BFS, DFS, hybrid BFS-DFS) and proposed optimizations.
result The hybrid BFS-DFS algorithm outperforms both BFS and DFS, and is more robust.
A fast numerical method for pricing double barrier options using Lagrange interpolation.
problem Pricing discrete double barrier knock-out call options efficiently.
method Approximating recursive solutions of the heat equation with Lagrange interpolation on Jacobi polynomials nodes.
result The method significantly reduces CPU time as the number of monitoring dates increases.
Unrealized Expectations: Comparing AI Methods vs Classical Algorithms for Maximum Independent Setcs.LG
AI methods often fail to outperform classical CPU-based solvers on Maximum Independent Set problems.
problem Comparing AI methods with classical CPU-based solvers on Maximum Independent Set problems.
method Comparison of AI methods (e.g., generative models, reinforcement learning) with classical CPU-based solvers (e.g., KaMIS) on Maximum Independent Set problem.
result AI-inspired methods are often outperformed by classical CPU-based solvers, even with post-processing techniques.
Signatory calculates signature and logsignature transforms efficiently on CPU and GPU.
problem Efficient computation of signature and logsignature transforms for machine learning.
method CPU and GPU parallelism, backpropagation, efficient precomputation strategies, algorithmic improvements.
result Substantial speedups on CPU and GPU, including real-world applications.
Benchmarking TPU, GPU, and CPU for deep learning models.
problem Improving performance in deep learning training.
method ParaDnn benchmark suite for FC, CNN, and RNN models on TPU, GPU, and CPU.
result TPU, GPU, and CPU have unique strengths for different types of models.
A new method reduces the cost of solving large-scale linear models.
problem Solving large-scale linear statistical models efficiently.
method Sequential Preconditioned Conjugate Gradient Method (SPCG).
result SPCG achieves OLS prediction accuracy with fewer iterations and less time.
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.
Improved RBM training speed using parallel computing.
problem High computational cost of training RBM models.
method Parallelized training using Horovod framework.
result Training time reduced to less than 12 minutes on 64 CPU nodes.
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.
Deep neural network detects anomalies in time series data.
problem Detecting anomalies in diverse time series data.
method End-to-end deep feedforward neural network without feature engineering.
result Effective anomaly detection for various time series types.
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))
CSM-NN uses neural networks to speed up and improve the accuracy of logic circuit simulations.
problem Inaccurate and slow simulation of complex circuits with billions of transistors.
method Current Source Model (CSM) combined with optimized neural network structures and parallel processing.
result Reduces simulation time by up to 6x on CPUs and 15x on GPUs with less than 2% error.
Framework optimizes cloud container sizing for ML tasks.
problem Challenges in cloud container configuration for ML services.
method Autonomous scaling using nested-loop Monte Carlo simulation.
result Reduces compute cost and accelerates ML algorithms.
RFX accelerates and compresses Random Forests for large datasets.
problem Memory bottleneck in proximity matrices limits Random Forest analysis.
method QLORA compression, CPU TriBlock storage, GPU batch sizing, 3D MDS visualization.
result Proximity-based Random Forest analysis on larger datasets is feasible.
The graphics processing unit (GPU) has emerged as a powerful and cost effective processor for general performance computing. GPUs are capable of an order of magnitude more floating-point operations per second as compared to modern central processing units (CPUs), and thus provide a great deal of promise for computation…
New method generates novel samples from closed-form diffusion models.
problem Closed-form SGMs memorize training data and cannot generate novel samples.
method Explicitly smooth closed-form score, use nearest-neighbor estimator.
result Efficient method generates novel samples without training.
CatBoost boosts performance on datasets with categorical features.
problem Handling categorical features in gradient boosting.
method Gradient boosting library with GPU and CPU implementations.
result Outperforms existing implementations on popular datasets.
Quantum computers can speed up machine learning optimization problems.
problem Long computation times and high resource requirements for classical optimization algorithms in machine learning.
method Developed a mathematical model to leverage quantum parallelism for machine learning.
result Quantum machine learning applied to a 3D time-varying image demonstrated significant speedup.
SySCD improves SCD scalability and speeds up training.
problem Scalability issues in parallel SCD algorithms.
method Developed a system-aware parallel SCD algorithm (SySCD) to avoid bottlenecks.
result Offers up to x42 speedup compared to state-of-the-art GLM solvers.
Optimizes parallel training of linear models, improving convergence.
problem Improving convergence of parallel training of linear models.
method Data partitioning scheme across threads to improve convergence.
result Achieved up to 42x speedup in convergence compared to state of the art implementations.
Ansor generates high-performance tensor programs for deep learning.
problem Generating high-performance tensor programs for deep learning on various hardware platforms is challenging.
method Ansor uses a hierarchical representation of the search space, sampling programs, and evolutionary search with a learned cost model to find high-performance programs.
result Ansor improves deep neural network execution performance up to 3.8x on Intel CPU, 2.6x on ARM CPU, and 1.7x on NVIDIA GPU.
Automated framework optimizes DNN deployment on Arm CPUs.
problem Lack of globally optimised DNN deployment across software levels.
method Reinforcement Learning search for automated design space exploration.
result Up to 4x improvement in performance and 2x reduction in memory.
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).
Python framework for distributed Keras training on multiple GPUs/CPU.
problem Efficiently training neural networks on multiple GPUs/CPU.
method Built on Keras, uses MPI for coordination, suitable for supercomputing.
result Demonstrated performance on various system sizes.
SuperNet speeds up neural network ensembling by training a single DNN for various phases.
problem High computational demand of ensembling large neural networks.
method Train a single DNN for multiple phases of training to represent various sub-models.
result SuperNet execution time comparable to single DNN training time plus coupling factors.
Kymatio simplifies scattering transforms for Python.
problem Signal processing and machine learning applications.
method Wavelet scattering transform implemented in Python.
result Efficient, GPU-accelerated implementation.
This work enhances GPS signals using robust GP regression for real-time high precision positioning.
problem Large noise in GPS signals degrades positioning accuracy.
method A three-step approach: signal validity tests, reweighting, and final model training.
result Proposed method achieves centimeter accuracy with 78.4% probability, outperforming other methods.
SparseTrain uses dynamic sparsity in training deep neural networks on CPUs.
problem Training deep neural networks efficiently on general-purpose processors.
method Exploits dynamic zeros introduced by ReLU in feature maps and gradients.
result Significantly speeds up training on CPUs, up to 1.51x.
Introduces BMF for efficient matrix factorization of large data.
problem Efficiently factorizing large scale matrices with limited memory.
method Uses block matrix approach and factorization at a block level.
result Demonstrates faster convergence on large matrices.
A lightweight model predicts IT system KPIs from historical data.
problem Predicting future KPIs of interconnected IT systems is hard due to diverse and changing components.
method A weighted heterogeneous ensemble method combining neural network and mean predictor.
result Achieved scores of 0.10 and 0.15 on test data.
FPGA-based logic architecture speeds up GBDT training 259x.
problem Training efficiency and power consumption in GBDT models.
method Implemented logic architecture on FPGA, compared with software libraries.
result Training speed 26-259x faster, power efficiency 90-1,104x higher.
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.
BoXHED2.0 boosts survival analysis for complex data.
problem Survival analysis with time-dependent covariates.
method Tree-boosted hazard estimator, fully nonparametric, scalable.
result Scalable to parametric boosted survival models in speed.
In January 3, 2009, Satoshi Nakamoto gave rise to the "Bitcoin Block Chain" creating the first block of the chain hashing on his computers central processing unit (CPU). Since then, the hash calculations to mine Bitcoin have been getting more and more complex, and consequently the mining hardware evolved to adapt to th…
Regression Trees and Random forest based feature selection for malaria risk exposure predictionstat.ML
This paper deals with prediction of anopheles number, the main vector of malaria risk, using environmental and climate variables. The variables selection is based on an automatic machine learning method using regression trees, and random forests combined with stratified two levels cross validation. The minimum threshol…
Paper proposes a new loss function for PU learning without negative examples.
problem Traditional machine learning struggles with negative examples, leading to biased predictions.
method Developed a collective loss function (cPU) for positive and unlabeled data.
result The cPU consistently outperforms existing methods in PU learning benchmarks and real-world datasets.