Accelerating Machine Learning Applications on Spark Using GPUs
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The document discusses the use of GPU acceleration in large-scale machine learning applications on Apache Spark, particularly focusing on optimized matrix factorization techniques. It presents the performance benefits of using GPUs, such as significant speed and cost efficiencies compared to traditional distributed solutions. Additionally, it outlines current and future work related to GPU acceleration for other machine learning algorithms.
Accelerating Machine Learning Applications on Spark Using GPUs
- 1. © 2015 IBM Corporation Accelerating Machine Learning Applications on Spark Using GPUs Wei Tan, Liana Fong Other contributors: Minisk Cho, Rajesh Bordawekar October 25
- 2. • IBM’s statements regarding its plans, directions, and intent are subject to change or withdrawal without notice at IBM’s sole discretion. • Information regarding potential future products is intended to outline our general product direction and it should not be relied on in making a purchasing decision. • The information mentioned regarding potential future products is not a commitment, promise, or legal obligation to deliver any material, code or functionality. Information about potential future products may not be incorporated into any contract. • The development, release, and timing of any future features or functionality described for our products remains at our sole discretion. Performance is based on measurements and projections using standard IBM benchmarks in a controlled environment. The actual throughput or performance that any user will experience will vary depending upon many factors, including considerations such as the amount of multiprogramming in the user’s job stream, the I/O configuration, the storage configuration, and the workload processed. Therefore, no assurance can be given that an individual user will achieve results similar to those stated here. Please Note: 2
- 3. Background: Apache Spark and MLlib • Apache Spark An in memory engine for large-scale data processing Used in database, stream, machine learning and graph processing 2 iter. 1 iter. 2 . . . Input
- 4. Background: Apache Spark and MLlib 3 Classification (LR, SVM…) Trees Recommendation Clustering … …
- 5. Background: GPU computing 4 Xeon e5 2687 CPU Tesla K40 GPU • Slower clock, fewer cache: not optimized for latency • More transistors to compute • Higher flops and memory bw • Optimized for data-parallel, high-throughput workload GPU is with:
- 6. Background: Apache Spark and MLlib 5 Classification (LR, SVM…) Trees Recommendation Clustering … … + (GPU) connectors and libs?
- 7. Problem: large-scale matrix factorization • Why Recommendation important in cognitive applications Digital ads market in US: 37.3 b*: Spark/Facebook/IBM Commerce Need a fast and scalable solution 6
- 8. Problem: large-scale matrix factorization • Why –Factorize the word co-occurrence matrix as rating matrix –Obtain word features that embeds semantics 7 man – woman = king – queen = brother – sister ….
- 9. MF: the state-of-art • Many systems optimized for medium- sized problems; very few target at huge problems. • Distributed solutions are slow. Do not roofline CPU performance Do not optimize communication • Distributed solutions need a lot of resources and cost. 8
- 10. MF: what we what to achieve • Scale to problems of any size. • Fast. • Cost-efficient. 9
- 11. Solution: cuMF - ALS on a machine with GPUs • On one GPU GPU (Nvidia K40): Memory BW: 288 GB/sec, compute: 5 Tflops Memory slower than compute need to optimize memory access! • The roofline model Higher Gflops higher op intensity (more flops per byte) caching! Operational intensity (Flops/Byte) Gflops/s 5T 1 288G × 17 ×
- 12. Solution: cuMF - ALS on a machine with GPUs • MO-ALS on one GPU: Memory-Optimized ALS •Access many θv columns: irregular due to R’s sparseness •Aggregate many θvθv Ts: memory intensive
- 13. Solution: cuMF - ALS on a machine with GPUs • Texture memory to smooth dis-contiguous, irregular memory access • Register memory to hold hotspot variables 12
- 14. Solution: cuMF - ALS on a machine with GPUs • On multiple GPUs • Exploit data & model parallelism – Data parallelism: solve using a portion of the training data – Model parallelism: solve a portion of the model • Exploit connection topology to minimize communication overhead 13 Data parallel model parallel
- 15. CuMF performance
- 16. CuMF Performance • cuMF: ALS on a single machine with 2* Nvidia K80 (4 cards) Compared with state-of-art distributed solutions • 6-10x as fast • 33-100x as cost-efficient (cuMF costs $2.5 per hour on Softlayer) Able to factorize the largest matrix ever reported 15
- 17. CuMF Performance • cuMF: ALS on a machine with one GPU 4x speedup as Spark ALS accelerator 16 Spark ALS Spark run-time MLlib cuMF with Spark cuMF C
- 18. Roadmap • Current work Impressive acceleration of MF with GPUs on one machine GPU acceleration techniques with model and data parallelism Illustrated applicability of GPU acceleration to Spark/Mllib Performance evaluations on K40, K80 GPUs, Intel and Power • Future work GPU acceleration of other ML algorithms in Mllib or others Acceleration of algorithms for multiple GPUs on single and across machines, with and without RDMA across machines Performance evaluation on other hardware, including • Other GPUs such as Nvidia Maxwell • Forthcoming NVLink connectively across GPUs within a single machine 17
- 19. 18 Notices and Disclaimers Copyright © 2015 by International Business Machines Corporation (IBM). No part of this document may be reproduced or transmitted in any form without written permission from IBM. U.S. Government Users Restricted Rights - Use, duplication or disclosure restricted by GSA ADP Schedule Contract with IBM. Information in these presentations (including information relating to products that have not yet been announced by IBM) has been reviewed for accuracy as of the date of initial publication and could include unintentional technical or typographical errors. IBM shall have no responsibility to update this information. THIS DOCUMENT IS DISTRIBUTED "AS IS" WITHOUT ANY WARRANTY, EITHER EXPRESS OR IMPLIED. IN NO EVENT SHALL IBM BE LIABLE FOR ANY DAMAGE ARISING FROM THE USE OF THIS INFORMATION, INCLUDING BUT NOT LIMITED TO, LOSS OF DATA, BUSINESS INTERRUPTION, LOSS OF PROFIT OR LOSS OF OPPORTUNITY. IBM products and services are warranted according to the terms and conditions of the agreements under which they are provided. Any statements regarding IBM's future direction, intent or product plans are subject to change or withdrawal without notice. Performance data contained herein was generally obtained in a controlled, isolated environments. Customer examples are presented as illustrations of how those customers have used IBM products and the results they may have achieved. Actual performance, cost, savings or other results in other operating environments may vary. References in this document to IBM products, programs, or services does not imply that IBM intends to make such products, programs or services available in all countries in which IBM operates or does business. Workshops, sessions and associated materials may have been prepared by independent session speakers, and do not necessarily reflect the views of IBM. All materials and discussions are provided for informational purposes only, and are neither intended to, nor shall constitute legal or other guidance or advice to any individual participant or their specific situation. It is the customer’s responsibility to insure its own compliance with legal requirements and to obtain advice of competent legal counsel as to the identification and interpretation of any relevant laws and regulatory requirements that may affect the customer’s business and any actions the customer may need to take to comply with such laws. IBM does not provide legal advice or represent or warrant that its services or products will ensure that the customer is in compliance with any law.
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