1,865 research outputs found
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
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Exploiting iteration-level parallelism in dataflow programs
The term "dataflow" generally encompasses three distinct aspects of computation - a data-driven model of computation, a functional/declarative programming language, and a special-purpose multiprocessor architecture. In this paper we decouple the language and architecture issues by demonstrating that declarative programming is a suitable vehicle for the programming of conventional distributed-memory multiprocessors.This is achieved by appling several transformations to the compiled declarative program to achieve iteration-level (rather than instruction-level) parallelism. The transformations first group individual instructions into sequential light-weight processes, and then insert primitives to: (1) cause array allocation to be distributed over multiple processors, (2) cause computation to follow the data distribution by inserting an index filtering mechanism into a given loop and spawning a copy of it on all PEs; the filter causes each instance of that loop to operate on a different subrange of the index variable.The underlying model of computation is a dataflow/von Neumann hybrid in that exection within a process is control-driven while the creation, blocking, and activation of processes is data-driven.The performance of this process-oriented dataflow system (PODS) is demonstrated using the hydrodynamics simulation benchmark called SIMPLE, where a 19-fold speedup on a 32-processor architecture has been achieved
Shared Arrangements: practical inter-query sharing for streaming dataflows
Current systems for data-parallel, incremental processing and view
maintenance over high-rate streams isolate the execution of independent
queries. This creates unwanted redundancy and overhead in the presence of
concurrent incrementally maintained queries: each query must independently
maintain the same indexed state over the same input streams, and new queries
must build this state from scratch before they can begin to emit their first
results. This paper introduces shared arrangements: indexed views of maintained
state that allow concurrent queries to reuse the same in-memory state without
compromising data-parallel performance and scaling. We implement shared
arrangements in a modern stream processor and show order-of-magnitude
improvements in query response time and resource consumption for interactive
queries against high-throughput streams, while also significantly improving
performance in other domains including business analytics, graph processing,
and program analysis
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