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blas-benchmarks/README.md
Andrzej Wójtowicz 107beaa3d2 added Intel Core i5-6500; 
changed results view of gcbd benchmark to relative performance gain;
changed reference CPU and GPU;
target architecture detection fixes
2016-07-14 17:26:58 +02:00

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# BLAS libraries benchmarks
Andrzej Wójtowicz
Document generation date: 2016-07-14 17:20:41
This document presents timing results for BLAS ([Basic Linear Algebra Subprograms](https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms)) libraries in [R](https://en.wikipedia.org/wiki/R_(programming_language)) on diverse CPUs and GPUs.
### Changelog
* 2016-07-14: **results:** added Intel Core i5-6500; changed results view of gcbd benchmark to relative performance gain; changed reference CPU (Intel Pentium Dual-Core E5300) and GPU (NVIDIA GeForce GT 630M); **code:** fixed target architecture detection for Intel Core i5-6500-like CPUs in multi-threaded Atlas library; added info how to force target architecture in GotoBLAS2 and BLIS libraries.
## Table of Contents
1. [Configuration](#configuration)
2. [Results per host](#results-per-host)
* [Intel Core i7-4790K + MSI GeForce GTX 980 Ti Lightning](#intel-core-i7-4790k--msi-geforce-gtx-980-ti-lightning)
* [Intel Core i5-4590 + NVIDIA GeForce GT 430](#intel-core-i5-4590--nvidia-geforce-gt-430)
* [Intel Core i5-4590 + NVIDIA GeForce GTX 750 Ti](#intel-core-i5-4590--nvidia-geforce-gtx-750-ti)
* [Intel Core i5-6500](#intel-core-i5-6500)
* [Intel Core i5-3570](#intel-core-i5-3570)
* [Intel Core i3-2120](#intel-core-i3-2120)
* [Intel Core i3-3120M](#intel-core-i3-3120m)
* [Intel Core i5-3317U + NVIDIA GeForce GT 630M](#intel-core-i5-3317u--nvidia-geforce-gt-630m)
* [Intel Pentium Dual-Core E5300](#intel-pentium-dual-core-e5300)
3. [Results per library](#results-per-library)
* [Netlib](#netlib)
* [Atlas (st)](#atlas-st)
* [OpenBLAS](#openblas)
* [Atlas (mt)](#atlas-mt)
* [GotoBLAS2](#gotoblas2)
* [MKL](#mkl)
* [BLIS](#blis)
* [cuBLAS](#cublas)
***
## Configuration
**OS**: [Debian](https://www.debian.org/) Jessie, kernel 4.4
**R software**: [Microsoft R Open](https://mran.microsoft.com/open/) (3.2.4)
**Libraries**:
|CPU (single-threaded)|CPU (multi-threaded)|GPU|
|---|---|---|
|[Netlib](http://www.netlib.org/) (debian package, blas 1.2.20110419, lapack 3.5.0)|[OpenBLAS](http://www.openblas.net/) (debian package, 0.2.12)|[NVIDIA cuBLAS](https://developer.nvidia.com/cublas) (NVBLAS 6.5 + Intel MKL)|
|[ATLAS](http://math-atlas.sourceforge.net/) (debian package, 3.10.2)|[ATLAS](http://math-atlas.sourceforge.net/) (dev branch, 3.11.38)| |
| |[GotoBLAS2](https://prs.ism.ac.jp/~nakama/SurviveGotoBLAS2/) (Survive fork, 3.141)| |
| |[Intel MKL](https://mran.microsoft.com/download/) (part of RevoMath package, 3.2.4)| |
| |[BLIS](https://github.com/flame/blis) (dev branch, 0.2.0+/17.05.2016)| |
**Hosts**:
|No.|CPU|GPU|
|---|---|---|
|1.|[Intel Core i7-4790K](http://ark.intel.com/products/80807/Intel-Core-i7-4790K-Processor-8M-Cache-up-to-4_40-GHz) (OC 4.5 GHz)|[MSI GeForce GTX 980 Ti Lightning](https://us.msi.com/Graphics-card/GTX-980-Ti-LIGHTNING.html#hero-specification)|
|2.|[Intel Core i5-4590](http://ark.intel.com/products/80815/Intel-Core-i5-4590-Processor-6M-Cache-up-to-3_70-GHz)|[NVIDIA GeForce GT 430](http://www.geforce.com/hardware/desktop-gpus/geforce-gt-430/specifications)|
|3.|[Intel Core i5-4590](http://ark.intel.com/products/80815/Intel-Core-i5-4590-Processor-6M-Cache-up-to-3_70-GHz)|[NVIDIA GeForce GTX 750 Ti](http://www.geforce.com/hardware/desktop-gpus/geforce-gtx-750-ti/specifications)|
|4.|[Intel Core i5-6500](http://ark.intel.com/products/88184/Intel-Core-i5-6500-Processor-6M-Cache-up-to-3_60-GHz)| - |
|5.|[Intel Core i5-3570](http://ark.intel.com/products/65702/Intel-Core-i5-3570-Processor-6M-Cache-up-to-3_80-GHz)| - |
|6.|[Intel Core i3-2120](http://ark.intel.com/products/53426/Intel-Core-i3-2120-Processor-3M-Cache-3_30-GHz)| - |
|7.|[Intel Core i3-3120M](http://ark.intel.com/products/71465/Intel-Core-i3-3120M-Processor-3M-Cache-2_50-GHz)| - |
|8.|[Intel Core i5-3317U](http://ark.intel.com/products/65707/Intel-Core-i5-3317U-Processor-3M-Cache-up-to-2_60-GHz)|[NVIDIA GeForce GT 630M](http://www.geforce.com/hardware/notebook-gpus/geforce-gt-630m/specifications)|
|9.|[Intel Pentium Dual-Core E5300](http://ark.intel.com/products/35300/Intel-Pentium-Processor-E5300-2M-Cache-2_60-GHz-800-MHz-FSB)| - |
**Benchmarks**: [R-benchmark-25](http://r.research.att.com/benchmarks/R-benchmark-25.R), [Revolution](https://gist.github.com/andrie/24c9672f1ea39af89c66#file-rro-mkl-benchmark-r), [Gcbd](https://cran.r-project.org/web/packages/gcbd/vignettes/gcbd.pdf).
# Results per host
## Intel Core i7-4790K + MSI GeForce GTX 980 Ti Lightning
### R-benchmark-25
#### 2800x2800 cross-product matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h1_b1_t1.png)
#### Linear regr. over a 2000x2000 matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h1_b1_t2.png)
#### Eigenvalues of a 600x600 random matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h1_b1_t3.png)
#### Determinant of a 2500x2500 random matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h1_b1_t4.png)
#### Cholesky decomposition of a 3000x3000 matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h1_b1_t5.png)
#### Inverse of a 1600x1600 random matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h1_b1_t6.png)
#### Escoufier's method on a 45x45 matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h1_b1_t7.png)
### Revolution benchmark
#### Matrix Multiply
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h1_b2_t1.png)
#### Cholesky Factorization
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h1_b2_t2.png)
#### Singular Value Deomposition
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h1_b2_t3.png)
#### Principal Components Analysis
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h1_b2_t4.png)
#### Linear Discriminant Analysis
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h1_b2_t5.png)
### Gcbd benchmark
#### Matrix Multiply
Performance gain regarding matrix size - reference: Netlib - from 50 to 5 runs - higher is better
![](gen/img/img_ph_h1_b3_t1.png)
#### QR Decomposition
Performance gain regarding matrix size - reference: Netlib - from 50 to 5 runs - higher is better
![](gen/img/img_ph_h1_b3_t2.png)
#### Singular Value Deomposition
Performance gain regarding matrix size - reference: Netlib - from 50 to 5 runs - higher is better
![](gen/img/img_ph_h1_b3_t3.png)
#### Triangular Decomposition
Performance gain regarding matrix size - reference: Netlib - from 50 to 5 runs - higher is better
![](gen/img/img_ph_h1_b3_t4.png)
## Intel Core i5-4590 + NVIDIA GeForce GT 430
### R-benchmark-25
#### 2800x2800 cross-product matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h2_b1_t1.png)
#### Linear regr. over a 2000x2000 matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h2_b1_t2.png)
#### Eigenvalues of a 600x600 random matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h2_b1_t3.png)
#### Determinant of a 2500x2500 random matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h2_b1_t4.png)
#### Cholesky decomposition of a 3000x3000 matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h2_b1_t5.png)
#### Inverse of a 1600x1600 random matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h2_b1_t6.png)
#### Escoufier's method on a 45x45 matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h2_b1_t7.png)
### Revolution benchmark
#### Matrix Multiply
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h2_b2_t1.png)
#### Cholesky Factorization
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h2_b2_t2.png)
#### Singular Value Deomposition
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h2_b2_t3.png)
#### Principal Components Analysis
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h2_b2_t4.png)
#### Linear Discriminant Analysis
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h2_b2_t5.png)
### Gcbd benchmark
#### Matrix Multiply
Performance gain regarding matrix size - reference: Netlib - from 50 to 5 runs - higher is better
![](gen/img/img_ph_h2_b3_t1.png)
#### QR Decomposition
Performance gain regarding matrix size - reference: Netlib - from 50 to 5 runs - higher is better
![](gen/img/img_ph_h2_b3_t2.png)
#### Singular Value Deomposition
Performance gain regarding matrix size - reference: Netlib - from 50 to 5 runs - higher is better
![](gen/img/img_ph_h2_b3_t3.png)
#### Triangular Decomposition
Performance gain regarding matrix size - reference: Netlib - from 50 to 5 runs - higher is better
![](gen/img/img_ph_h2_b3_t4.png)
## Intel Core i5-4590 + NVIDIA GeForce GTX 750 Ti
### R-benchmark-25
#### 2800x2800 cross-product matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h3_b1_t1.png)
#### Linear regr. over a 2000x2000 matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h3_b1_t2.png)
#### Eigenvalues of a 600x600 random matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h3_b1_t3.png)
#### Determinant of a 2500x2500 random matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h3_b1_t4.png)
#### Cholesky decomposition of a 3000x3000 matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h3_b1_t5.png)
#### Inverse of a 1600x1600 random matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h3_b1_t6.png)
#### Escoufier's method on a 45x45 matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h3_b1_t7.png)
### Revolution benchmark
#### Matrix Multiply
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h3_b2_t1.png)
#### Cholesky Factorization
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h3_b2_t2.png)
#### Singular Value Deomposition
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h3_b2_t3.png)
#### Principal Components Analysis
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h3_b2_t4.png)
#### Linear Discriminant Analysis
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h3_b2_t5.png)
### Gcbd benchmark
#### Matrix Multiply
Performance gain regarding matrix size - reference: Netlib - from 50 to 5 runs - higher is better
![](gen/img/img_ph_h3_b3_t1.png)
#### QR Decomposition
Performance gain regarding matrix size - reference: Netlib - from 50 to 5 runs - higher is better
![](gen/img/img_ph_h3_b3_t2.png)
#### Singular Value Deomposition
Performance gain regarding matrix size - reference: Netlib - from 50 to 5 runs - higher is better
![](gen/img/img_ph_h3_b3_t3.png)
#### Triangular Decomposition
Performance gain regarding matrix size - reference: Netlib - from 50 to 5 runs - higher is better
![](gen/img/img_ph_h3_b3_t4.png)
## Intel Core i5-6500
### R-benchmark-25
#### 2800x2800 cross-product matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h4_b1_t1.png)
#### Linear regr. over a 2000x2000 matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h4_b1_t2.png)
#### Eigenvalues of a 600x600 random matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h4_b1_t3.png)
#### Determinant of a 2500x2500 random matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h4_b1_t4.png)
#### Cholesky decomposition of a 3000x3000 matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h4_b1_t5.png)
#### Inverse of a 1600x1600 random matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h4_b1_t6.png)
#### Escoufier's method on a 45x45 matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h4_b1_t7.png)
### Revolution benchmark
#### Matrix Multiply
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h4_b2_t1.png)
#### Cholesky Factorization
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h4_b2_t2.png)
#### Singular Value Deomposition
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h4_b2_t3.png)
#### Principal Components Analysis
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h4_b2_t4.png)
#### Linear Discriminant Analysis
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h4_b2_t5.png)
### Gcbd benchmark
#### Matrix Multiply
Performance gain regarding matrix size - reference: Netlib - from 50 to 5 runs - higher is better
![](gen/img/img_ph_h4_b3_t1.png)
#### QR Decomposition
Performance gain regarding matrix size - reference: Netlib - from 50 to 5 runs - higher is better
![](gen/img/img_ph_h4_b3_t2.png)
#### Singular Value Deomposition
Performance gain regarding matrix size - reference: Netlib - from 50 to 5 runs - higher is better
![](gen/img/img_ph_h4_b3_t3.png)
#### Triangular Decomposition
Performance gain regarding matrix size - reference: Netlib - from 50 to 5 runs - higher is better
![](gen/img/img_ph_h4_b3_t4.png)
## Intel Core i5-3570
### R-benchmark-25
#### 2800x2800 cross-product matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h5_b1_t1.png)
#### Linear regr. over a 2000x2000 matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h5_b1_t2.png)
#### Eigenvalues of a 600x600 random matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h5_b1_t3.png)
#### Determinant of a 2500x2500 random matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h5_b1_t4.png)
#### Cholesky decomposition of a 3000x3000 matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h5_b1_t5.png)
#### Inverse of a 1600x1600 random matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h5_b1_t6.png)
#### Escoufier's method on a 45x45 matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h5_b1_t7.png)
### Revolution benchmark
#### Matrix Multiply
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h5_b2_t1.png)
#### Cholesky Factorization
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h5_b2_t2.png)
#### Singular Value Deomposition
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h5_b2_t3.png)
#### Principal Components Analysis
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h5_b2_t4.png)
#### Linear Discriminant Analysis
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h5_b2_t5.png)
### Gcbd benchmark
#### Matrix Multiply
Performance gain regarding matrix size - reference: Netlib - from 50 to 5 runs - higher is better
![](gen/img/img_ph_h5_b3_t1.png)
#### QR Decomposition
Performance gain regarding matrix size - reference: Netlib - from 50 to 5 runs - higher is better
![](gen/img/img_ph_h5_b3_t2.png)
#### Singular Value Deomposition
Performance gain regarding matrix size - reference: Netlib - from 50 to 5 runs - higher is better
![](gen/img/img_ph_h5_b3_t3.png)
#### Triangular Decomposition
Performance gain regarding matrix size - reference: Netlib - from 50 to 5 runs - higher is better
![](gen/img/img_ph_h5_b3_t4.png)
## Intel Core i3-2120
### R-benchmark-25
#### 2800x2800 cross-product matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h6_b1_t1.png)
#### Linear regr. over a 2000x2000 matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h6_b1_t2.png)
#### Eigenvalues of a 600x600 random matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h6_b1_t3.png)
#### Determinant of a 2500x2500 random matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h6_b1_t4.png)
#### Cholesky decomposition of a 3000x3000 matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h6_b1_t5.png)
#### Inverse of a 1600x1600 random matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h6_b1_t6.png)
#### Escoufier's method on a 45x45 matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h6_b1_t7.png)
### Revolution benchmark
#### Matrix Multiply
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h6_b2_t1.png)
#### Cholesky Factorization
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h6_b2_t2.png)
#### Singular Value Deomposition
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h6_b2_t3.png)
#### Principal Components Analysis
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h6_b2_t4.png)
#### Linear Discriminant Analysis
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h6_b2_t5.png)
### Gcbd benchmark
#### Matrix Multiply
Performance gain regarding matrix size - reference: Netlib - from 50 to 5 runs - higher is better
![](gen/img/img_ph_h6_b3_t1.png)
#### QR Decomposition
Performance gain regarding matrix size - reference: Netlib - from 50 to 5 runs - higher is better
![](gen/img/img_ph_h6_b3_t2.png)
#### Singular Value Deomposition
Performance gain regarding matrix size - reference: Netlib - from 50 to 5 runs - higher is better
![](gen/img/img_ph_h6_b3_t3.png)
#### Triangular Decomposition
Performance gain regarding matrix size - reference: Netlib - from 50 to 5 runs - higher is better
![](gen/img/img_ph_h6_b3_t4.png)
## Intel Core i3-3120M
### R-benchmark-25
#### 2800x2800 cross-product matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h7_b1_t1.png)
#### Linear regr. over a 2000x2000 matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h7_b1_t2.png)
#### Eigenvalues of a 600x600 random matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h7_b1_t3.png)
#### Determinant of a 2500x2500 random matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h7_b1_t4.png)
#### Cholesky decomposition of a 3000x3000 matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h7_b1_t5.png)
#### Inverse of a 1600x1600 random matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h7_b1_t6.png)
#### Escoufier's method on a 45x45 matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h7_b1_t7.png)
### Revolution benchmark
#### Matrix Multiply
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h7_b2_t1.png)
#### Cholesky Factorization
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h7_b2_t2.png)
#### Singular Value Deomposition
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h7_b2_t3.png)
#### Principal Components Analysis
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h7_b2_t4.png)
#### Linear Discriminant Analysis
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h7_b2_t5.png)
### Gcbd benchmark
#### Matrix Multiply
Performance gain regarding matrix size - reference: Netlib - from 50 to 5 runs - higher is better
![](gen/img/img_ph_h7_b3_t1.png)
#### QR Decomposition
Performance gain regarding matrix size - reference: Netlib - from 50 to 5 runs - higher is better
![](gen/img/img_ph_h7_b3_t2.png)
#### Singular Value Deomposition
Performance gain regarding matrix size - reference: Netlib - from 50 to 5 runs - higher is better
![](gen/img/img_ph_h7_b3_t3.png)
#### Triangular Decomposition
Performance gain regarding matrix size - reference: Netlib - from 50 to 5 runs - higher is better
![](gen/img/img_ph_h7_b3_t4.png)
## Intel Core i5-3317U + NVIDIA GeForce GT 630M
### R-benchmark-25
#### 2800x2800 cross-product matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h8_b1_t1.png)
#### Linear regr. over a 2000x2000 matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h8_b1_t2.png)
#### Eigenvalues of a 600x600 random matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h8_b1_t3.png)
#### Determinant of a 2500x2500 random matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h8_b1_t4.png)
#### Cholesky decomposition of a 3000x3000 matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h8_b1_t5.png)
#### Inverse of a 1600x1600 random matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h8_b1_t6.png)
#### Escoufier's method on a 45x45 matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h8_b1_t7.png)
### Revolution benchmark
#### Matrix Multiply
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h8_b2_t1.png)
#### Cholesky Factorization
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h8_b2_t2.png)
#### Singular Value Deomposition
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h8_b2_t3.png)
#### Principal Components Analysis
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h8_b2_t4.png)
#### Linear Discriminant Analysis
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h8_b2_t5.png)
### Gcbd benchmark
#### Matrix Multiply
Performance gain regarding matrix size - reference: Netlib - from 50 to 5 runs - higher is better
![](gen/img/img_ph_h8_b3_t1.png)
#### QR Decomposition
Performance gain regarding matrix size - reference: Netlib - from 50 to 5 runs - higher is better
![](gen/img/img_ph_h8_b3_t2.png)
#### Singular Value Deomposition
Performance gain regarding matrix size - reference: Netlib - from 50 to 5 runs - higher is better
![](gen/img/img_ph_h8_b3_t3.png)
#### Triangular Decomposition
Performance gain regarding matrix size - reference: Netlib - from 50 to 5 runs - higher is better
![](gen/img/img_ph_h8_b3_t4.png)
## Intel Pentium Dual-Core E5300
### R-benchmark-25
#### 2800x2800 cross-product matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h9_b1_t1.png)
#### Linear regr. over a 2000x2000 matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h9_b1_t2.png)
#### Eigenvalues of a 600x600 random matrix
BLIS hangs in this test
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h9_b1_t3.png)
#### Determinant of a 2500x2500 random matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h9_b1_t4.png)
#### Cholesky decomposition of a 3000x3000 matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h9_b1_t5.png)
#### Inverse of a 1600x1600 random matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h9_b1_t6.png)
#### Escoufier's method on a 45x45 matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h9_b1_t7.png)
### Revolution benchmark
#### Matrix Multiply
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h9_b2_t1.png)
#### Cholesky Factorization
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h9_b2_t2.png)
#### Singular Value Deomposition
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h9_b2_t3.png)
#### Principal Components Analysis
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h9_b2_t4.png)
#### Linear Discriminant Analysis
Time in seconds - 10 runs - lower is better
![](gen/img/img_ph_h9_b2_t5.png)
### Gcbd benchmark
#### Matrix Multiply
Performance gain regarding matrix size - reference: Netlib - from 50 to 5 runs - higher is better
![](gen/img/img_ph_h9_b3_t1.png)
#### QR Decomposition
Performance gain regarding matrix size - reference: Netlib - from 50 to 5 runs - higher is better
![](gen/img/img_ph_h9_b3_t2.png)
#### Singular Value Deomposition
Performance gain regarding matrix size - reference: Netlib - from 50 to 5 runs - higher is better
![](gen/img/img_ph_h9_b3_t3.png)
#### Triangular Decomposition
Performance gain regarding matrix size - reference: Netlib - from 50 to 5 runs - higher is better
![](gen/img/img_ph_h9_b3_t4.png)
# Results per library
## Netlib
### R-benchmark-25
#### 2800x2800 cross-product matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l1_b1_t1.png)
#### Linear regr. over a 2000x2000 matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l1_b1_t2.png)
#### Eigenvalues of a 600x600 random matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l1_b1_t3.png)
#### Determinant of a 2500x2500 random matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l1_b1_t4.png)
#### Cholesky decomposition of a 3000x3000 matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l1_b1_t5.png)
#### Inverse of a 1600x1600 random matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l1_b1_t6.png)
#### Escoufier's method on a 45x45 matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l1_b1_t7.png)
### Revolution benchmark
#### Matrix Multiply
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l1_b2_t1.png)
#### Cholesky Factorization
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l1_b2_t2.png)
#### Singular Value Deomposition
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l1_b2_t3.png)
#### Principal Components Analysis
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l1_b2_t4.png)
#### Linear Discriminant Analysis
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l1_b2_t5.png)
### Gcbd benchmark
#### Matrix Multiply
Performance gain regarding matrix size - reference: Intel Pentium Dual-Core E5300 - from 50 to 5 runs - higher is better
![](gen/img/img_pl_l1_b3_t1.png)
#### QR Decomposition
Performance gain regarding matrix size - reference: Intel Pentium Dual-Core E5300 - from 50 to 5 runs - higher is better
![](gen/img/img_pl_l1_b3_t2.png)
#### Singular Value Deomposition
Performance gain regarding matrix size - reference: Intel Pentium Dual-Core E5300 - from 50 to 5 runs - higher is better
![](gen/img/img_pl_l1_b3_t3.png)
#### Triangular Decomposition
Performance gain regarding matrix size - reference: Intel Pentium Dual-Core E5300 - from 50 to 5 runs - higher is better
![](gen/img/img_pl_l1_b3_t4.png)
## ATLAS (st)
### R-benchmark-25
#### 2800x2800 cross-product matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l2_b1_t1.png)
#### Linear regr. over a 2000x2000 matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l2_b1_t2.png)
#### Eigenvalues of a 600x600 random matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l2_b1_t3.png)
#### Determinant of a 2500x2500 random matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l2_b1_t4.png)
#### Cholesky decomposition of a 3000x3000 matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l2_b1_t5.png)
#### Inverse of a 1600x1600 random matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l2_b1_t6.png)
#### Escoufier's method on a 45x45 matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l2_b1_t7.png)
### Revolution benchmark
#### Matrix Multiply
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l2_b2_t1.png)
#### Cholesky Factorization
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l2_b2_t2.png)
#### Singular Value Deomposition
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l2_b2_t3.png)
#### Principal Components Analysis
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l2_b2_t4.png)
#### Linear Discriminant Analysis
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l2_b2_t5.png)
### Gcbd benchmark
#### Matrix Multiply
Performance gain regarding matrix size - reference: Intel Pentium Dual-Core E5300 - from 50 to 5 runs - higher is better
![](gen/img/img_pl_l2_b3_t1.png)
#### QR Decomposition
Performance gain regarding matrix size - reference: Intel Pentium Dual-Core E5300 - from 50 to 5 runs - higher is better
![](gen/img/img_pl_l2_b3_t2.png)
#### Singular Value Deomposition
Performance gain regarding matrix size - reference: Intel Pentium Dual-Core E5300 - from 50 to 5 runs - higher is better
![](gen/img/img_pl_l2_b3_t3.png)
#### Triangular Decomposition
Performance gain regarding matrix size - reference: Intel Pentium Dual-Core E5300 - from 50 to 5 runs - higher is better
![](gen/img/img_pl_l2_b3_t4.png)
## OpenBLAS
### R-benchmark-25
#### 2800x2800 cross-product matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l3_b1_t1.png)
#### Linear regr. over a 2000x2000 matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l3_b1_t2.png)
#### Eigenvalues of a 600x600 random matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l3_b1_t3.png)
#### Determinant of a 2500x2500 random matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l3_b1_t4.png)
#### Cholesky decomposition of a 3000x3000 matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l3_b1_t5.png)
#### Inverse of a 1600x1600 random matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l3_b1_t6.png)
#### Escoufier's method on a 45x45 matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l3_b1_t7.png)
### Revolution benchmark
#### Matrix Multiply
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l3_b2_t1.png)
#### Cholesky Factorization
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l3_b2_t2.png)
#### Singular Value Deomposition
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l3_b2_t3.png)
#### Principal Components Analysis
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l3_b2_t4.png)
#### Linear Discriminant Analysis
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l3_b2_t5.png)
### Gcbd benchmark
#### Matrix Multiply
Performance gain regarding matrix size - reference: Intel Pentium Dual-Core E5300 - from 50 to 5 runs - higher is better
![](gen/img/img_pl_l3_b3_t1.png)
#### QR Decomposition
Performance gain regarding matrix size - reference: Intel Pentium Dual-Core E5300 - from 50 to 5 runs - higher is better
![](gen/img/img_pl_l3_b3_t2.png)
#### Singular Value Deomposition
Performance gain regarding matrix size - reference: Intel Pentium Dual-Core E5300 - from 50 to 5 runs - higher is better
![](gen/img/img_pl_l3_b3_t3.png)
#### Triangular Decomposition
Performance gain regarding matrix size - reference: Intel Pentium Dual-Core E5300 - from 50 to 5 runs - higher is better
![](gen/img/img_pl_l3_b3_t4.png)
## ATLAS (mt)
### R-benchmark-25
#### 2800x2800 cross-product matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l4_b1_t1.png)
#### Linear regr. over a 2000x2000 matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l4_b1_t2.png)
#### Eigenvalues of a 600x600 random matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l4_b1_t3.png)
#### Determinant of a 2500x2500 random matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l4_b1_t4.png)
#### Cholesky decomposition of a 3000x3000 matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l4_b1_t5.png)
#### Inverse of a 1600x1600 random matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l4_b1_t6.png)
#### Escoufier's method on a 45x45 matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l4_b1_t7.png)
### Revolution benchmark
#### Matrix Multiply
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l4_b2_t1.png)
#### Cholesky Factorization
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l4_b2_t2.png)
#### Singular Value Deomposition
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l4_b2_t3.png)
#### Principal Components Analysis
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l4_b2_t4.png)
#### Linear Discriminant Analysis
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l4_b2_t5.png)
### Gcbd benchmark
#### Matrix Multiply
Performance gain regarding matrix size - reference: Intel Pentium Dual-Core E5300 - from 50 to 5 runs - higher is better
![](gen/img/img_pl_l4_b3_t1.png)
#### QR Decomposition
Performance gain regarding matrix size - reference: Intel Pentium Dual-Core E5300 - from 50 to 5 runs - higher is better
![](gen/img/img_pl_l4_b3_t2.png)
#### Singular Value Deomposition
Performance gain regarding matrix size - reference: Intel Pentium Dual-Core E5300 - from 50 to 5 runs - higher is better
![](gen/img/img_pl_l4_b3_t3.png)
#### Triangular Decomposition
Performance gain regarding matrix size - reference: Intel Pentium Dual-Core E5300 - from 50 to 5 runs - higher is better
![](gen/img/img_pl_l4_b3_t4.png)
## GotoBLAS2
### R-benchmark-25
#### 2800x2800 cross-product matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l5_b1_t1.png)
#### Linear regr. over a 2000x2000 matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l5_b1_t2.png)
#### Eigenvalues of a 600x600 random matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l5_b1_t3.png)
#### Determinant of a 2500x2500 random matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l5_b1_t4.png)
#### Cholesky decomposition of a 3000x3000 matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l5_b1_t5.png)
#### Inverse of a 1600x1600 random matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l5_b1_t6.png)
#### Escoufier's method on a 45x45 matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l5_b1_t7.png)
### Revolution benchmark
#### Matrix Multiply
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l5_b2_t1.png)
#### Cholesky Factorization
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l5_b2_t2.png)
#### Singular Value Deomposition
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l5_b2_t3.png)
#### Principal Components Analysis
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l5_b2_t4.png)
#### Linear Discriminant Analysis
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l5_b2_t5.png)
### Gcbd benchmark
#### Matrix Multiply
Performance gain regarding matrix size - reference: Intel Pentium Dual-Core E5300 - from 50 to 5 runs - higher is better
![](gen/img/img_pl_l5_b3_t1.png)
#### QR Decomposition
Performance gain regarding matrix size - reference: Intel Pentium Dual-Core E5300 - from 50 to 5 runs - higher is better
![](gen/img/img_pl_l5_b3_t2.png)
#### Singular Value Deomposition
Performance gain regarding matrix size - reference: Intel Pentium Dual-Core E5300 - from 50 to 5 runs - higher is better
![](gen/img/img_pl_l5_b3_t3.png)
#### Triangular Decomposition
Performance gain regarding matrix size - reference: Intel Pentium Dual-Core E5300 - from 50 to 5 runs - higher is better
![](gen/img/img_pl_l5_b3_t4.png)
## MKL
### R-benchmark-25
#### 2800x2800 cross-product matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l6_b1_t1.png)
#### Linear regr. over a 2000x2000 matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l6_b1_t2.png)
#### Eigenvalues of a 600x600 random matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l6_b1_t3.png)
#### Determinant of a 2500x2500 random matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l6_b1_t4.png)
#### Cholesky decomposition of a 3000x3000 matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l6_b1_t5.png)
#### Inverse of a 1600x1600 random matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l6_b1_t6.png)
#### Escoufier's method on a 45x45 matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l6_b1_t7.png)
### Revolution benchmark
#### Matrix Multiply
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l6_b2_t1.png)
#### Cholesky Factorization
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l6_b2_t2.png)
#### Singular Value Deomposition
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l6_b2_t3.png)
#### Principal Components Analysis
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l6_b2_t4.png)
#### Linear Discriminant Analysis
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l6_b2_t5.png)
### Gcbd benchmark
#### Matrix Multiply
Performance gain regarding matrix size - reference: Intel Pentium Dual-Core E5300 - from 50 to 5 runs - higher is better
![](gen/img/img_pl_l6_b3_t1.png)
#### QR Decomposition
Performance gain regarding matrix size - reference: Intel Pentium Dual-Core E5300 - from 50 to 5 runs - higher is better
![](gen/img/img_pl_l6_b3_t2.png)
#### Singular Value Deomposition
Performance gain regarding matrix size - reference: Intel Pentium Dual-Core E5300 - from 50 to 5 runs - higher is better
![](gen/img/img_pl_l6_b3_t3.png)
#### Triangular Decomposition
Performance gain regarding matrix size - reference: Intel Pentium Dual-Core E5300 - from 50 to 5 runs - higher is better
![](gen/img/img_pl_l6_b3_t4.png)
## BLIS
### R-benchmark-25
#### 2800x2800 cross-product matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l7_b1_t1.png)
#### Linear regr. over a 2000x2000 matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l7_b1_t2.png)
#### Eigenvalues of a 600x600 random matrix
Intel Pentium Dual-Core E5300 hangs in this test
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l7_b1_t3.png)
#### Determinant of a 2500x2500 random matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l7_b1_t4.png)
#### Cholesky decomposition of a 3000x3000 matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l7_b1_t5.png)
#### Inverse of a 1600x1600 random matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l7_b1_t6.png)
#### Escoufier's method on a 45x45 matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l7_b1_t7.png)
### Revolution benchmark
#### Matrix Multiply
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l7_b2_t1.png)
#### Cholesky Factorization
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l7_b2_t2.png)
#### Singular Value Deomposition
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l7_b2_t3.png)
#### Principal Components Analysis
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l7_b2_t4.png)
#### Linear Discriminant Analysis
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l7_b2_t5.png)
### Gcbd benchmark
#### Matrix Multiply
Performance gain regarding matrix size - reference: Intel Pentium Dual-Core E5300 - from 50 to 5 runs - higher is better
![](gen/img/img_pl_l7_b3_t1.png)
#### QR Decomposition
Performance gain regarding matrix size - reference: Intel Pentium Dual-Core E5300 - from 50 to 5 runs - higher is better
![](gen/img/img_pl_l7_b3_t2.png)
#### Singular Value Deomposition
Performance gain regarding matrix size - reference: Intel Pentium Dual-Core E5300 - from 50 to 5 runs - higher is better
![](gen/img/img_pl_l7_b3_t3.png)
#### Triangular Decomposition
Performance gain regarding matrix size - reference: Intel Pentium Dual-Core E5300 - from 50 to 5 runs - higher is better
![](gen/img/img_pl_l7_b3_t4.png)
## cuBLAS
### R-benchmark-25
#### 2800x2800 cross-product matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l8_b1_t1.png)
#### Linear regr. over a 2000x2000 matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l8_b1_t2.png)
#### Eigenvalues of a 600x600 random matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l8_b1_t3.png)
#### Determinant of a 2500x2500 random matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l8_b1_t4.png)
#### Cholesky decomposition of a 3000x3000 matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l8_b1_t5.png)
#### Inverse of a 1600x1600 random matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l8_b1_t6.png)
#### Escoufier's method on a 45x45 matrix
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l8_b1_t7.png)
### Revolution benchmark
#### Matrix Multiply
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l8_b2_t1.png)
#### Cholesky Factorization
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l8_b2_t2.png)
#### Singular Value Deomposition
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l8_b2_t3.png)
#### Principal Components Analysis
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l8_b2_t4.png)
#### Linear Discriminant Analysis
Time in seconds - 10 runs - lower is better
![](gen/img/img_pl_l8_b2_t5.png)
### Gcbd benchmark
#### Matrix Multiply
Performance gain regarding matrix size - reference: NVIDIA GeForce GT 630M - from 50 to 5 runs - higher is better
![](gen/img/img_pl_l8_b3_t1.png)
#### QR Decomposition
Performance gain regarding matrix size - reference: NVIDIA GeForce GT 630M - from 50 to 5 runs - higher is better
![](gen/img/img_pl_l8_b3_t2.png)
#### Singular Value Deomposition
Performance gain regarding matrix size - reference: NVIDIA GeForce GT 630M - from 50 to 5 runs - higher is better
![](gen/img/img_pl_l8_b3_t3.png)
#### Triangular Decomposition
Performance gain regarding matrix size - reference: NVIDIA GeForce GT 630M - from 50 to 5 runs - higher is better
![](gen/img/img_pl_l8_b3_t4.png)
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