## Best practices, news, tips and tricks - learn about Oracle's R Technologies for Oracle Database and Big Data

• January 18, 2013

# Oracle R Distribution Performance Benchmark

Senior Director, Data Science and Machine Learning

# Oracle R Distribution PerformanceBenchmarks

Oracle R Distribution provides
dramatic performance gains with MKL

Using the recognized R benchmark R-benchmark-25.R test script,
we compared the performance of Oracle
R Distribution
with and without the dynamically loaded high performance Math Kernel Library (MKL) from
Intel
. The benchmark
results show Oracle R Distribution is significantly faster with the dynamically
loaded high performance library. R users can immediately gain performance enhancements
over open source R, analyzing data on 64-bit architectures and leveraging
parallel processing within specific R functions that invoke computations
performed by these high performance libraries.

The Community-developed
test consists of matrix calculations and functions, program control, matrix multiplication,
Cholesky Factorization, Singular Value Decomposition (SVD), Principal Component
Analysis (PCA), and Linear Discriminant Analysis. Such computations form a core
component of many real-world problems, often taking the majority of compute
time. The ability to speed up these computations means faster results for
faster decision making.

While the benchmark results reported were conducted
using Intel MKL, Oracle R Distribution
also supports AMD Core Math Library (ACML) and Solaris Sun Performance Library.

Oracle R Distribution 2.15.1 x64 Benchmark Results (time in seconds)

 ORD with internal BLAS/LAPACK1 thread ORD + MKL 1 thread ORD + MKL2 threads ORD + MKL4 threads ORD + MKL8 threads Performance gain ORD + MKL 4 threads Performance gain ORD + MKL 8 threads Matrix Calculations 11.2 1.9 1.3 1.1 0.9 9.2x 11.4x Matrix Functions 7.2 1.1 0.6 0.4 0.4 17.0x 17.0x Program Control 1.4 1.3 1.5 1.4 0.8 0.0x 0.8x Matrix Multiply 517.6 21.2 10.9 5.8 3.1 88.2x 166.0x Cholesky Factorization 25 3.9 2.1 1.3 0.8 18.2x 29.4x Singular Value Decomposition 103.5 15.1 7.8 4.9 3.4 20.1x 40.9x Principal Component Analysis 490.1 42.7 24.9 15.9 11.7 29.8x 40.9x Linear Discriminant Analysis 419.8 120.9 110.8 94.1 88.0 3.5x 3.8x

This benchmark was executed on a 3-node cluster, with 24 cores at 3.07GHz
per CPU and 47 GB RAM, using Linux 5.5.

In the first graph, we see significant performance improvements. For example, SVD with ORD plus MKL executes 20 times faster using 4 threads, and 29 times faster using 8 threads. For Cholesky Factorization, ORD plus MKL is 18 and 30 times faster for 4 and 8 threads, respectively.

In the second graph,we focus on the three longer running tests. Matrix multiplication is 88 and 166 times faster for 4 and 8 threads, respectively. PCA is 30 and 50 times faster, and LDA is over 3 times faster.

This level of performance improvement can significantly reduce application execution time and make interactive, dynamically generated results readily achievable. Note that ORD plus MKL not only impacts performance on the client side, but also when used in combination with R scripts executed using Oracle R Enterprise Embedded R Execution. Such R scripts, executing at the database server machine, reap these performance gains as well.

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