12 research outputs found
Wavelets and wavelet-like transforms on the sphere and their application to geophysical data inversion
Many flexible parameterizations exist to represent data on the sphere. In
addition to the venerable spherical harmonics, we have the Slepian basis,
harmonic splines, wavelets and wavelet-like Slepian frames. In this paper we
focus on the latter two: spherical wavelets developed for geophysical
applications on the cubed sphere, and the Slepian "tree", a new construction
that combines a quadratic concentration measure with wavelet-like
multiresolution. We discuss the basic features of these mathematical tools, and
illustrate their applicability in parameterizing large-scale global geophysical
(inverse) problems.Comment: 15 pages, 11 figures, submitted to the Proceedings of the SPIE 2011
conference Wavelets and Sparsity XI
Kepler: Robust Learning for Faster Parametric Query Optimization
Most existing parametric query optimization (PQO) techniques rely on
traditional query optimizer cost models, which are often inaccurate and result
in suboptimal query performance. We propose Kepler, an end-to-end
learning-based approach to PQO that demonstrates significant speedups in query
latency over a traditional query optimizer. Central to our method is Row Count
Evolution (RCE), a novel plan generation algorithm based on perturbations in
the sub-plan cardinality space. While previous approaches require accurate cost
models, we bypass this requirement by evaluating candidate plans via actual
execution data and training an ML model to predict the fastest plan given
parameter binding values. Our models leverage recent advances in neural network
uncertainty in order to robustly predict faster plans while avoiding
regressions in query performance. Experimentally, we show that Kepler achieves
significant improvements in query runtime on multiple datasets on PostgreSQL.Comment: SIGMOD 202
Dynamic Control Flow in Large-Scale Machine Learning
Many recent machine learning models rely on fine-grained dynamic control flow
for training and inference. In particular, models based on recurrent neural
networks and on reinforcement learning depend on recurrence relations,
data-dependent conditional execution, and other features that call for dynamic
control flow. These applications benefit from the ability to make rapid
control-flow decisions across a set of computing devices in a distributed
system. For performance, scalability, and expressiveness, a machine learning
system must support dynamic control flow in distributed and heterogeneous
environments.
This paper presents a programming model for distributed machine learning that
supports dynamic control flow. We describe the design of the programming model,
and its implementation in TensorFlow, a distributed machine learning system.
Our approach extends the use of dataflow graphs to represent machine learning
models, offering several distinctive features. First, the branches of
conditionals and bodies of loops can be partitioned across many machines to run
on a set of heterogeneous devices, including CPUs, GPUs, and custom ASICs.
Second, programs written in our model support automatic differentiation and
distributed gradient computations, which are necessary for training machine
learning models that use control flow. Third, our choice of non-strict
semantics enables multiple loop iterations to execute in parallel across
machines, and to overlap compute and I/O operations.
We have done our work in the context of TensorFlow, and it has been used
extensively in research and production. We evaluate it using several real-world
applications, and demonstrate its performance and scalability.Comment: Appeared in EuroSys 2018. 14 pages, 16 figure