![Linear Regression Conjugate Gradient
(preliminary 1/2)
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64 128 256 512 1024 2048
TimeinSeconds
No. of Rows of input matrix (in Thousands)
PPC CPU Time
PPC GPU Time
x86 CPU Time
x86 GPU Time
Data: random with sparsity 0.95, 1000 features
Icpt: 0, maxi: 20, tol: 0.001, reg: 0.01
Driver-memory: 100G, local[*] master
M-V multiplication
chain is memory bound,
But more cores help
with parallelization.](https://image.slidesharecdn.com/systemmldlmeetup-180327060402/85/System-mldl-meetup-7-320.jpg)
![Linear Regression Conjugate Gradient
(preliminary 2/2)
8
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64 256 1024
TimeinSeconds
No. of Rows of input matrix (in Thousands)
PPC GPU Time
x86 GPU Time
Data: random with sparsity 0.95, 1000 features
Icpt: 0, maxi: 20, tol: 0.001, reg: 0.01
Driver-memory: 100G, local[*] master
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64 256 1024
TimeinSeconds
No. of Rows of input matrix (in Thousands)
CPU-GPU Transfer Time
PPC toDev Time
x86 toDev Time
Most of the time is spent
in transferring data from
host to device
-> 2x performance benefit
due to CPU-GPU NVLink](https://image.slidesharecdn.com/systemmldlmeetup-180327060402/85/System-mldl-meetup-8-320.jpg)
System mldl meetup
- 1. Scalable Machine/Deep Learning with Apache SystemML on Power 1
- 2. Why Apache SystemML Today’s Roles of Data Scientists – Algorithm researcher: Invent new optimization schemes – Systems programmer: provide distributed implementations – Deployment engineer: Run for varying datasets – Systems researcher: Optimize clusters SystemML simplifies the Life of Data Scientists – in implementing custom machine learning – running algorithms distributed if needed – running algorithms varying from small data to large data NIPS ICML KDD JMLR 2
- 3. Apache SystemML – Declarative Machine Learning Productivity of data scientists – Machine learning language for data scientists (“The SQL for analytics”) – Strong foundation in linear algebra and statistical functions – Comes with approx. 20+ algorithms pre-implemented – Enable Solutions development and Tools Scalability & Performance – Built on data parallel platforms, e.g. Spark Cost-based optimizer to compile execution plans – Depending on data characteristics (tall/skinny, short/wide) and cluster characteristics – Ranging from in-memory single node to clusters (MapReduce, Spark), and hybrid plans APIs & Tools – Command line: standalone Java app, spark-submit, hadoop jar – Use in Spark through Scala, Python, R, and Java APIs – Embeddable scoring library – Tools: REPL (Scala Spark and pyspark), SparkR, SparkML, Jupyter, Zeppelin Notebooks Hadoop or Spark Cluster (scale-out) In-Memory Single Node (scale-up) Runtime Compiler Language GPU backend In progress 3
- 4. Apache SystemML Open Source Apache Open source Project (http://systemml.apache.org/) – Nov. 2015, Start SystemML Apache Incubator Project – … – Feb. 2017, Release 0.12.0 on Spark 1.6.x …, Python API. May 2017, Release 0.14.0 on Spark 2.0.2+. – May 2017, Apache Top Level Project – Sep 2017, Release 0.15 Release downloads (http://systemml.apache.org/download) – Binaries – Coordinates to Maven repository Github source code (https://github.com/apache/systemml) Documentation (https://apache.github.io/systemml/) 3 Hours KDD Hands-On Tutorial (http://systemml.apache.org/tutorial- kdd2017.html), Aug. 2017 4
- 5. 5 https://github.com/apache/systemml/blob/master/samples/jupyter- notebooks/Deep_Learning_Image_Classification.ipynb Handwritten Digits Image Classification Using LeNet CNN
- 6. SystemML on Power Environment Contributed native ppc64le libraries for Jcuda to mavenized jcuda project – GPU backend on Power for SystemML Contributed native ppc64le libraries to protoc project – Useful for compiling Caffe proto files Supported native BLAS operations in SystemML – Matrix Multiplication, Convolution (forward/backward) – OpenBLAS with OpenMP support 6
- 7. Linear Regression Conjugate Gradient (preliminary 1/2) 7 0 2 4 6 8 10 12 14 64 128 256 512 1024 2048 TimeinSeconds No. of Rows of input matrix (in Thousands) PPC CPU Time PPC GPU Time x86 CPU Time x86 GPU Time Data: random with sparsity 0.95, 1000 features Icpt: 0, maxi: 20, tol: 0.001, reg: 0.01 Driver-memory: 100G, local[*] master M-V multiplication chain is memory bound, But more cores help with parallelization.
- 8. Linear Regression Conjugate Gradient (preliminary 2/2) 8 0 2 4 6 8 10 12 14 64 256 1024 TimeinSeconds No. of Rows of input matrix (in Thousands) PPC GPU Time x86 GPU Time Data: random with sparsity 0.95, 1000 features Icpt: 0, maxi: 20, tol: 0.001, reg: 0.01 Driver-memory: 100G, local[*] master 0 1 2 3 4 5 6 7 64 256 1024 TimeinSeconds No. of Rows of input matrix (in Thousands) CPU-GPU Transfer Time PPC toDev Time x86 toDev Time Most of the time is spent in transferring data from host to device -> 2x performance benefit due to CPU-GPU NVLink
- 9. More Details Matthias Boehm, Alexandre Evfimievski, Niketan Pansare, Berthold Reinwald, Prithvi Sen: Declarative, Large-Scale Machine Learning with Apache SystemML, 3 hours hands-on tutorial, KDD 2017 Tarek Elgamal, Shangyu Luo, Matthias Boehm, Alexandre V. Evfimievski, Shirish Tatikonda, Berthold Reinwald, Prithviray Sen: SPOOF: Sum- Product Optimization and Operator Fusion for Large-Scale Machine Learning. CIDR 2017 Ahmed Elgohary, Matthias Boehm, Peter J. Haas, Frederick R. Reiss, Berthold Reinwald: Compressed Linear Algebra for Large Scale Machine Learning. VLDB 2016 (Best Paper Award) – Extended Version to appear in VLDB Journal, 2017 – Summary Version to appear in ACM SIGMOD Record Research Highlights, 2017 Matthias Boehm, Michael W. Dusenberry, Deron Eriksson, Alexandre V. Evfimievski, Faraz Makari Manshadi, Niketan Pansare, Berthold Reinwald, Frederick R. Reiss, Prithviraj Sen, Arvind C. Surve, Shirish Tatikonda. SystemML: Declarative Machine Learning on Spark. VLDB 2016 Botong Huang, Matthias Boehm, Yuanyuan Tian, Berthold Reinwald, Shirish Tatikonda, Frederick R. Reiss: Resource Elasticity for Large- Scale Machine Learning. SIGMOD 2015: 137-152 Arash Ashari, Shirish Tatikonda, Matthias Boehm, Berthold Reinwald, Keith Campbell, John Keenleyside, P. Sadayappan: On optimizing machine learning workloads via kernel fusion. PPOPP 2015: 173-182 Sebastian Schelter, Juan Soto, Volker Markl, Douglas Burdick, Berthold Reinwald, Alexandre V. Evfimievski: Efficient sample generation for scalable meta learning. ICDE 2015: 1191-1202 Matthias Boehm, Douglas R. Burdick, Alexandre V. Evfimievski, Berthold Reinwald, Frederick R. Reiss, Prithviraj Sen, Shirish Tatikonda, Yuanyuan Tian: SystemML's Optimizer: Plan Generation for Large-Scale Machine Learning Programs. IEEE Data Eng. Bull. 37(3): 52-62 (2014) Matthias Boehm, Shirish Tatikonda, Berthold Reinwald, Prithviraj Sen, Yuanyuan Tian, Douglas Burdick, Shivakumar Vaithyanathan: Hybrid Parallelization Strategies for Large-Scale Machine Learning in SystemML. PVLDB 7(7): 553-564 (2014) Peter D. Kirchner, Matthias Boehm, Berthold Reinwald, Daby M. Sow, Michael Schmidt, Deepak S. Turaga, Alain Biem: Large Scale Discriminative Metric Learning. IPDPS Workshop 2014: 1656-1663 Yuanyuan Tian, Shirish Tatikonda, Berthold Reinwald: Scalable and Numerically Stable Descriptive Statistics in SystemML. ICDE 2012: 1351- 1359 Amol Ghoting, Rajasekar Krishnamurthy, Edwin P. D. Pednault, Berthold Reinwald, Vikas Sindhwani, Shirish Tatikonda, Yuanyuan Tian, Shivakumar Vaithyanathan: SystemML: Declarative machine learning on MapReduce. ICDE 2011: 231-242 Custom Algorithm Optimizer Resource Elasticity GPU Sampling Numeric Stability Task Parallelism 1st paper on Spark Compression Automatic Rewr & Fusion 9 Hands on Tutorial
- 10. Summary SystemML simplifies the Life of Data Scientist Custom Machine/Deep Learning Algorithms Scale up & out Mixed Workloads – Memory access bound – Compute bound Strike Balance between – Data transfer – Parallelism 10
Editor's Notes
- #4: 3