6,990 research outputs found
PRETZEL: Opening the Black Box of Machine Learning Prediction Serving Systems
Machine Learning models are often composed of pipelines of transformations.
While this design allows to efficiently execute single model components at
training time, prediction serving has different requirements such as low
latency, high throughput and graceful performance degradation under heavy load.
Current prediction serving systems consider models as black boxes, whereby
prediction-time-specific optimizations are ignored in favor of ease of
deployment. In this paper, we present PRETZEL, a prediction serving system
introducing a novel white box architecture enabling both end-to-end and
multi-model optimizations. Using production-like model pipelines, our
experiments show that PRETZEL is able to introduce performance improvements
over different dimensions; compared to state-of-the-art approaches PRETZEL is
on average able to reduce 99th percentile latency by 5.5x while reducing memory
footprint by 25x, and increasing throughput by 4.7x.Comment: 16 pages, 14 figures, 13th USENIX Symposium on Operating Systems
Design and Implementation (OSDI), 201
Relay: A New IR for Machine Learning Frameworks
Machine learning powers diverse services in industry including search,
translation, recommendation systems, and security. The scale and importance of
these models require that they be efficient, expressive, and portable across an
array of heterogeneous hardware devices. These constraints are often at odds;
in order to better accommodate them we propose a new high-level intermediate
representation (IR) called Relay. Relay is being designed as a
purely-functional, statically-typed language with the goal of balancing
efficient compilation, expressiveness, and portability. We discuss the goals of
Relay and highlight its important design constraints. Our prototype is part of
the open source NNVM compiler framework, which powers Amazon's deep learning
framework MxNet
Toolflows for Mapping Convolutional Neural Networks on FPGAs: A Survey and Future Directions
In the past decade, Convolutional Neural Networks (CNNs) have demonstrated
state-of-the-art performance in various Artificial Intelligence tasks. To
accelerate the experimentation and development of CNNs, several software
frameworks have been released, primarily targeting power-hungry CPUs and GPUs.
In this context, reconfigurable hardware in the form of FPGAs constitutes a
potential alternative platform that can be integrated in the existing deep
learning ecosystem to provide a tunable balance between performance, power
consumption and programmability. In this paper, a survey of the existing
CNN-to-FPGA toolflows is presented, comprising a comparative study of their key
characteristics which include the supported applications, architectural
choices, design space exploration methods and achieved performance. Moreover,
major challenges and objectives introduced by the latest trends in CNN
algorithmic research are identified and presented. Finally, a uniform
evaluation methodology is proposed, aiming at the comprehensive, complete and
in-depth evaluation of CNN-to-FPGA toolflows.Comment: Accepted for publication at the ACM Computing Surveys (CSUR) journal,
201
Tupleware: Redefining Modern Analytics
There is a fundamental discrepancy between the targeted and actual users of
current analytics frameworks. Most systems are designed for the data and
infrastructure of the Googles and Facebooks of the world---petabytes of data
distributed across large cloud deployments consisting of thousands of cheap
commodity machines. Yet, the vast majority of users operate clusters ranging
from a few to a few dozen nodes, analyze relatively small datasets of up to a
few terabytes, and perform primarily compute-intensive operations. Targeting
these users fundamentally changes the way we should build analytics systems.
This paper describes the design of Tupleware, a new system specifically aimed
at the challenges faced by the typical user. Tupleware's architecture brings
together ideas from the database, compiler, and programming languages
communities to create a powerful end-to-end solution for data analysis. We
propose novel techniques that consider the data, computations, and hardware
together to achieve maximum performance on a case-by-case basis. Our
experimental evaluation quantifies the impact of our novel techniques and shows
orders of magnitude performance improvement over alternative systems
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