Remove obsolete bench/ and btl/ directories

libeigen/eigen!2217

Co-authored-by: Rasmus Munk Larsen <rmlarsen@gmail.com>

doc/DenseDecompositionBenchmark.dox

@@ -35,7 +35,7 @@ Timings are in \b milliseconds, and factors are relative to the LLT decompositio
  + For large problem sizes, only the decomposition implementing a cache-friendly blocking strategy scale well. Those include LLT, PartialPivLU, HouseholderQR, and BDCSVD. This explain why for a 4k x 4k matrix, HouseholderQR is faster than LDLT. In the future, LDLT and ColPivHouseholderQR will also implement blocking strategies.
  + CompleteOrthogonalDecomposition is based on ColPivHouseholderQR and they thus achieve the same level of performance.
 
-The above table has been generated by the <a href="https://gitlab.com/libeigen/eigen/raw/master/bench/dense_solvers.cpp">bench/dense_solvers.cpp</a> file, feel-free to hack it to generate a table matching your hardware, compiler, and favorite problem sizes.
+The above table was originally generated by a benchmark tool. Feel free to write your own benchmark to generate a table matching your hardware, compiler, and favorite problem sizes.
 
 */
 

doc/SparseLinearSystems.dox

@@ -176,7 +176,7 @@ x2 = solver.solve(b2);
 For direct methods, the solution are computed at the machine precision. Sometimes, the solution need not be too accurate. In this case, the iterative methods are more suitable and the desired accuracy can be set before the solve step using \b setTolerance(). For all the available functions, please, refer to the documentation of the \link IterativeLinearSolvers_Module Iterative solvers module \endlink. 
 
 \section BenchmarkRoutine
-Most of the time, all you need is to know how much time it will take to solve your system, and hopefully, what is the most suitable solver. In Eigen, we provide a benchmark routine that can be used for this purpose. It is very easy to use. In the build directory, navigate to `bench/spbench` and compile the routine by typing `make spbenchsolver`. Run it with `--help` option to get the list of all available options. Basically, the matrices to test should be in <a href="http://math.nist.gov/MatrixMarket/formats.html">MatrixMarket Coordinate format</a>, and the routine returns the statistics from all available solvers in Eigen.
+Most of the time, all you need is to know how much time it will take to solve your system, and hopefully, what is the most suitable solver. You can benchmark solvers by timing them on matrices in <a href="http://math.nist.gov/MatrixMarket/formats.html">MatrixMarket Coordinate format</a>. See the `benchmarks/Sparse/` directory for example benchmarks.
 
 To export your matrices and right-hand-side vectors in the matrix-market format, you can use the unsupported SparseExtra module:
 \code

doc/UsingAOCL.dox

@@ -196,14 +196,10 @@ cmake .. -DCMAKE_BUILD_TYPE=Release \
 \endcode
 
 
-To build Eigen with AOCL integration and benchmarking capabilities, use the following CMake configuration:
+To build Eigen with AOCL integration, use the following CMake configuration:
 
 \code
-cmake .. -DEIGEN_BUILD_AOCL_BENCH=ON \
-         -DEIGEN_AOCL_BENCH_FLAGS="-O3 -mavx512f -fveclib=AMDLIBM" \
-         -DEIGEN_AOCL_BENCH_USE_MT=OFF \
-         -DEIGEN_AOCL_BENCH_ARCH=znver5 \
-         -DCMAKE_BUILD_TYPE=Debug \
+cmake .. -DCMAKE_BUILD_TYPE=Release \
          -DCMAKE_C_COMPILER=clang \
          -DCMAKE_CXX_COMPILER=clang++ \
          -DCMAKE_INSTALL_PREFIX=$PWD/install \
@@ -215,10 +211,6 @@ cmake .. -DEIGEN_BUILD_AOCL_BENCH=ON \
 
 <table class="manual">
 <tr><th>Parameter</th><th>Expected Values</th><th>Description</th></tr>
-<tr><td>\c EIGEN_BUILD_AOCL_BENCH</td><td>\c ON, \c OFF</td><td>Enable/disable AOCL benchmark compilation</td></tr>
-<tr class="alt"><td>\c EIGEN_AOCL_BENCH_FLAGS</td><td>Compiler flags string</td><td>Additional compiler optimizations: \c "-O3 -mavx512f -fveclib=AMDLIBM"</td></tr>
-<tr><td>\c EIGEN_AOCL_BENCH_USE_MT</td><td>\c ON, \c OFF</td><td>Use multi-threaded AOCL libraries (\c ON recommended for performance)</td></tr>
-<tr class="alt"><td>\c EIGEN_AOCL_BENCH_ARCH</td><td>\c znver3, \c znver4, \c znver5, \c native, \c generic</td><td>Target AMD architecture (match your CPU generation)</td></tr>
 <tr><td>\c CMAKE_BUILD_TYPE</td><td>\c Release, \c Debug, \c RelWithDebInfo</td><td>Build configuration (\c Release recommended for benchmarks)</td></tr>
 <tr class="alt"><td>\c CMAKE_C_COMPILER</td><td>\c clang, \c gcc</td><td>C compiler (clang recommended for AOCL)</td></tr>
 <tr><td>\c CMAKE_CXX_COMPILER</td><td>\c clang++, \c g++</td><td>C++ compiler (clang++ recommended for AOCL)</td></tr>