finished readme, fixed name in cmake

gemm/mklblas/CMakeLists.txt

@@ -16,7 +16,7 @@ if ("${DWARF_PREFIX}" STREQUAL "")
   set(DWARF_PREFIX 1_dense)
 endif()
 
-set(NAME ${DWARF_PREFIX}_gemm_tbb_lapack)
+set(NAME ${DWARF_PREFIX}_mklblas)
 
 if (OPENMP_FOUND AND Boost_FOUND AND MKL_FOUND)
 	enable_language(CXX)

gemm/mklblas/README.md

+# README - MKL Blas Gemm Example
+
+## Description
+
+Matrix multiplications can be done in different ways which vary in performance. This example should point out how massive these performance differences really are.
+
+This code sample demonstrates:
+ * Different implementations to do a matrix multiplication
+   * Serial
+     * IJK Algorithm
+     * IKJ Algorithm
+   * Parallel
+     * OpenMP parallelized IKJ Algorithm
+     * Intel MKL BLAS Library
+ * Benchmark implementations with different matrix dimensions.
+
+Benchmark design:
+
+The program runs the multiplication with matrix dimensions from 2-2048 with single precision floats. For timing accuracy multiple repetitions are used and the average time per multiplication is calculated. The program output is a CSV-Text with dimension size and time per multiplication in seconds.
+
+## Release Date
+
+2016-10-24
+
+## Version History
+
+ * 2016-10-24: Initial Release on PRACE CodeVault repository
+
+## Contributors
+
+ * Thomas Steinreiter - [thomas.steinreiter@risc-software.at](mailto:thomas.steinreiter@risc-software.at)
+
+
+## Copyright
+
+This code is available under Apache License, Version 2.0 - see also the license file in the CodeVault root directory.
+
+
+## Languages
+
+This sample is entirely written in C++14.
+
+## Parallelisation
+
+This sample uses MPI-3 for parallelization.
+
+## Level of the code sample complexity
+
+Intermediate
+
+## Compiling
+
+Follow the compilation instructions given in the main directory of the kernel samples directory (`/hpc_kernel_samples/README.md`).
+
+## Running
+
+In your current working directory, to run the program you may use something similar to
+
+```
+1_dense_mklblas blas
+```
+
+either on the command line or in your batch script.
+
+
+### Command line arguments
+
+ * `<mode>`: Specity the implementation used for the multiplication (obligatory)
+   * `serialijk` Use the serial IJK Algorithm 
+   * `serialikj` Use the serial IKJ Algorithm
+   * `parallel` Use the parallel (OpenMp) IKJ Algorithm
+   * `serialijk` Use the parallel Intel MKL Library
+
+### Example
+
+If you run
+
+```
+1_dense_mklblas blas
+```
+
+the output should look similar to
+
+```
+2;6.59846e-06
+4;6.6475e-06
+8;5.93214e-06
+16;6.80383e-06
+32;8.44547e-06
+64;2.10678e-05
+128;0.000102738
+256;0.000789877
+512;0.00560496
+1024;0.0301494
+2048;0.338072
+```
+
+### Benchmarks
+
+Tested on Intel Core i7-6820HQ @ 2,70GHz (4 physical cores, 8 logical).
+Compiler: Intel(R) 64, Version 17.0.0.109 Build 20160721 (MKL 2017.0) on Windows 10 1607.
+
+![chart](hpc_kernel_samples/dense_linear_algebra/gemm/mklblas/benchmarks/Chart.PNG)
+
+Be aware the chart has logarithmic scale.
+
+The benchmarks show some interesting facts:
+ * The *serial IKJ* implementation is brutally faster than IJK (~30x @2048)
+ * *Serial IKJ* is the fastest smaller sizes (2-16). *MKL Blas* is the fastest for larger sizes (32-2048).
+ * The *Parallel OpenMP IKJ implementation* is never the fastest. **Manually parallelizing matrix multiplication is not recommended!**
\ No newline at end of file