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Found 6 papers, 2 papers with code
Sequential and Shared-Memory Parallel Algorithms for Partitioned Local Depths
In this work, we design, analyze, and optimize sequential and shared-memory parallel algorithms for partitioned local depths (PaLD).
Avoiding Communication in Logistic Regression
Stochastic gradient descent (SGD) is one of the most widely used optimization methods for solving various machine learning problems.
Avoiding Synchronization in First-Order Methods for Sparse Convex Optimization
Parallel computing has played an important role in speeding up convex optimization methods for big data analytics and large-scale machine learning (ML).
AdaBatch: Adaptive Batch Sizes for Training Deep Neural Networks
Training deep neural networks with Stochastic Gradient Descent, or its variants, requires careful choice of both learning rate and batch size.
Avoiding Communication in Proximal Methods for Convex Optimization Problems
We formulate the algorithm for two different optimization methods on the Lasso problem and show that the latency cost is reduced by a factor of k while bandwidth and floating-point operation costs remain the same.
Matrix Factorization at Scale: a Comparison of Scientific Data Analytics in Spark and C+MPI Using Three Case Studies
We explore the trade-offs of performing linear algebra using Apache Spark, compared to traditional C and MPI implementations on HPC platforms.
Distributed, Parallel, and Cluster Computing G.1.3; C.2.4
