psblas3/test/computational_routines

Computational Routines Test

This is a directory containing all the tests done in order to analyze the correctness of the computational routines present in PSBLAS [3].

Test Environment

These tests are developed using a linux environment, in particular Rocky Linux 9.5 (Blue Onyx).

The compiler used is:

• gnu 12.2.1

The necessary dependnces are:

• mpich 4.2.2
• PSBLAS 3.9
• CUDA 12.5

Test Approach

In order to check wheter each kernel computation is correct or not, it was taken into account a simple approach resported in [1]: the kernels are excecuted both in single y_{s} and double precision y_{d}. The difference between the two results \Delta y should not exceed the machine epsilon of the single precision floating point representation. This quantity is identified as the unit roundoff u. In this the IEEE floating point representation we have u = 2^-24 \approx 5.96 \cdot 10^{-8} and therefore \Delta y = y_d - y_s \leq u as stated in Highman in his book [2]. It is also important to note that \Delta y is a double precision floating point number, since it should be able to detect an higher precision with respect to a single precision representation.

The innovative approach introduced in this test suite is to have a theoretical results showing us the correctness of the double precision implementation. In fact, the double precision computation is used as validation result for the single precision one, but no assumption of correctness were done before. In this work, double precision computations are validated using a heuristic approach based on the number p of significand digits that can be estimated using the \gamma_n = \frac{nu}{1-nu} worst case constant known from Higman [2] in order to have an upper bound to the number of significand digits. Since this approach is kernel specific, see each test directory to see how this idea is applied to each routine.

Directory description

Each directory has the name of the computational kernel routines described in the documentation of the version 3.9 of the PSBLAS library. In each directory there are different files:

• autotest.sh, it's the bash script excecuting and also possibily compiling kernel specific tests.
• Makefile, contains all the rules to compile the subdir including PSBLAS modules and files, also according to the PSBLAS testing environment utilities.
• psb_<kernel_name>_test.f90, a single file implementing a main program that initializes the testing environment and start test routine parametrizing over the test input space. In this file it also contained a subroutine containing the call to the actual kernel using the parameters passed from the main program.
• ```README.md``, a file explaining the input space and all the choices used to validete and explain the tests implemented.

Routines

In this test suite were considered only computational routines implemented by PSBLAS, according to the version 3.9 of the documentation. In the following table are reported all the kernels, their implementation and wheter or not they were tested yet.

Kernel	PSBLAS Subroutine	Description	Single Process Test	Multi-Process Test	Complex Test	GPU Test
General Dense Matrix Sum	psb_geaxpby	This subroutine is an interface to the computational kernel for dense matrix sum: Y \leftarrow \alpha X + \beta Y	Yes ✅	Yes ✅	No ❌	No ❌
Dot product	psb_gedot	This function computes dot product between two vectors x and y. dot \leftarrow x^T y If x and y are real vectors it computes dot-product as: dot \leftarrow x^H y	Yes ✅	Yes ✅	No ❌	No ❌
Generalized Dot Product	psb_gedots	This subroutine computes a series of dot products among the columns of two dense matrices x and y:res(i) \leftarrow x(:,i)^T y(:,i) If the matrices are complex, then the usual convention applies, i.e. the conjugate transpose of x is used. If x and y are of rank one, then res is a scalar, else it is a rank one array.	Yes ✅	Yes ✅	No ❌	No ❌
Infinity-Norm of Vector	psb_normi/psb_geamax	This function computes the infinity-norm of a vector x. If x is a real vector it computes infinity norm as:amax \leftarrow max \mid x_i \mid$else if x is a complex vector then it computes the infinity-norm as: $amax \leftarrow max(\mid re(x_i) \mid + \mid im(x_i) \mid)	No ❌	No ❌	No ❌	No ❌
Generalized Infinity Norm	psb_geamaxs	This subroutine computes a series of infinity norms on the columns of a dense matrix x: res(i) \leftarrow max_k \mid x(k,i) \mid	No ❌	No ❌	No ❌	No ❌
1-Norm of Vector	psb_norm1 / psb_geasums	This function computes the 1-norm of a vector x. If x is a real vector it computes 1-norm as: asum \leftarrow \mid \mid x_i \mid \mid else if x is a complex vector then it computes 1-norm as: asum \leftarrow \mid \mid re(x) \mid \mid_1 + \mid \mid im(x) \mid \mid_1	No ❌	No ❌	No ❌	No ❌
Generalized 1-Norm of Vector	psb_geasums	This subroutine computes a series of 1-norms on the columns of a dense matrix x: res(i) \leftarrow max_k \mid x(k,i) \mid This function computes the 1-norm of a vector x. If x is a real vector it computes 1-norm as: res(i) \leftarrow \mid \mid x_i \mid \mid$else if x is a complex vector then it computes 1-norm as:$res(i) \leftarrow \mid \mid re(x) \mid \mid_\ + \mid \mid im(x) \mid \mid_1	No ❌	No ❌	No ❌	No ❌
2-Norm of Vector	psb_norm2 / psb_genrm2	This function computes the 2-norm of a vector x. If x is a real vector it computes 2-norm as: nrm2 \leftarrow \sqrt{x^T x} else if x is a complex vector then it computes 2-norm as: nrm2 \leftarrow \sqrt{x^H x}	No ❌	No ❌	No ❌	No ❌
Generalized 2-Norm of Vector	psb_genrm2s / psb_spnrm1	This subroutine computes a series of 2-norms on the columns of a dense matrix x: res(i) \leftarrow \mid \mid x(:,i) \mid \mid_2	No ❌	No ❌	No ❌	No ❌
1-Norm of Sparse Matrix	psb_norm1	This function computes the 1-norm of a matrix A: nrm1 \leftarrow \mid \mid A \mid \mid_1 where A represents the global matrix A	No ❌	No ❌	No ❌	No ❌
Infinity Norm of Sparse Matrix	psb_normi / psb_spnrmi	This function computes the infinity-norm of a matrix A: nrmi \leftarrow \mid \mid A \mid \mid_{\infty} where: A represents the global matrix	No ❌	No ❌	No ❌	No ❌
Sparse Matrix by Dense Matrix Product	psb_spmm	This subroutine computes the Sparse Matrix by Dense Matrix Product: y \leftarrow \alpha A x + \beta y y \leftarrow \alpha A^T x + \beta y y \leftarrow \alpha A^H x + \beta y where: x is the global dense matrix x_{:,:} y is the global dense matrix y_{:,:} A is the global sparse matrix	Work in progress 🛠️	No ❌	No ❌	No ❌
Triangular System Solve	psb_spsm	This subroutine computes the Triangular System Solve: y \leftarrow \alpha T^{-1} x + \beta y y \leftarrow \alpha D^{-1} x + \beta y y \leftarrow \alpha T^{-1} D x + \beta y y \leftarrow \alpha T^{-T} x + \beta y y \leftarrow \alpha D T^{-T} x + \beta y y \leftarrow \alpha T^{-T} D x + \beta y y \leftarrow \alpha T^{-H} x + \beta y y \leftarrow \alpha D T^{-H} x + \beta y y \leftarrow \alpha T^{-H} D x + \beta y where: x is the global dense matrix x_{:,:} y is the global dense matrix y_{:,:} T is the global sparse block triangular submatrix D is the scaling diagonal matrix	No ❌	No ❌	No ❌	No ❌
Entrywise Product	psb_gemlt	This function computes the entrywise product between two vectors x and y dot \leftarrow x(i)y(i)	No ❌	No ❌	No ❌	No ❌
Entrywise Division	psb_gediv	This function computes the entrywise division between two vectors x and y div \leftarrow \frac{x(i)}{y(i)}	No ❌	No ❌	No ❌	No ❌
Entrywise Inversion	psb_geinv	This function computes the entrywise inverse of a vector x and puts it into y inv \leftarrow \frac{1}{x(i)}	No ❌	No ❌	No ❌	No ❌

Developer Notes

In order to keep compliant the excecution of the bash script used to automate the teest excecution, remember to create a new directory to put new tests and to use the name convention of psb_test_ signature for utilities functions and psb_kernel_test for tests used for new routines.

References

[1]. Higham, Nicholas J. Testing linear algebra software. Springer US, 1997

[2]. Higham, Nicholas J. Accuracy and stability of numerical algorithms. Society for industrial and applied mathematics, 2002.

[3], Filippone, Salvatore, and Michele Colajanni. "PSBLAS: A library for parallel linear algebra computation on sparse matrices." ACM Transactions on Mathematical Software (TOMS) 26.4 (2000): 527-550.