Design Documentation#
This document is intended for advanced developers that want to understand, modify or extend the functionality of the rocSPARSE library.
The rocSPARSE library is developed using the Hourglass API approach. This provides a thin C89 API while still having all the convenience of C++. As a side effect, ABI related binary compatibility issues can be avoided. Furthermore, this approach allows rocSPARSE routines to be used by other programming languages.
In public API header files, rocSPARSE only relies on functions, pointers, forward declared structs, enumerations and type defs. rocSPARSE introduces multiple library and object handles by using opaque types to hide layout and implementation details from the user.
Temporary Device Memory#
Many routines exposed by the rocSPARSE API require a temporary storage buffer on the device. You are responsible for buffer allocation and deallocation.
Hence, allocated buffers can be re-used and do not need to be regularly (de)allocated on every single API call.
For this purpose, routines that require a temporary storage buffer offer a special API function to query for the storage buffer size, for example rocsparse_scsrsv_buffer_size()
.
Library Source Organization#
The following is the structure of the rocSPARSE library in the GitHub repository.
library/include/ directory#
The library/include directory contains all files that are exposed to the user. The rocSPARSE API, is declared here.
File |
Description |
---|---|
rocsparse.h |
Includes all other API related rocSPARSE header files. |
rocsparse-auxiliary.h |
Declares all rocSPARSE auxiliary functions, such as handle and descriptor management. |
rocsparse-complex-types.h |
Defines the rocSPARSE complex data types rocsparse_float_complex and rocsparse_double_complex. |
rocsparse-functions.h |
Declares all rocSPARSE Sparse Linear Algebra Subroutines of Level1, Level2, Level3, Extra, Preconditioner, Format Conversion, Reordering, Generic and Utility. Achieved by including headers from the library/include/internal directory. |
rocsparse-types.h |
Defines all data types used by rocSPARSE. |
rocsparse-version.h.in |
Provides the configured version and settings that is initially set by CMake during compilation. |
The library/include/internal directory contains the public API for all rocSPARSE Sparse Linear Algebra Subroutines organized into Level1, Level2, Level3, Extra, Preconditioner, Format Conversion, Reordering, Generic and Utility directories.
library/src/ directory#
This directory contains all rocSPARSE library source files. The root of the library/src/ directory hosts the implementation of the library handle and auxiliary functions. Each sub-directory is responsible for the specific class of sparse linear algebra subroutines. The library/src/include directory defines Commonly Shared Device-Code, Status-Flag Macros, The rocsparse_mat_info Structure and Activity Logging.
File |
Description |
---|---|
handle.cpp |
Implementation of opaque handle structures. |
rocsparse_auxiliary.cpp |
Implementation of auxiliary functions, e.g. create and destroy handles. |
status.cpp |
Implementation of |
include/common.h |
Commonly used functions among several rocSPARSE routines, see Commonly Shared Device-Code. |
include/definitions.h |
Status-flag macros are defined here, see Status-Flag Macros. |
include/handle.h |
Declaration of opaque handle structures. |
include/logging.h |
Implementation of different rocSPARSE logging helper functions. |
include/status.h |
Declaration of |
include/utility.h |
Implementation of different rocSPARSE logging functionality. |
clients/ directory#
This directory contains all clients, e.g. samples, unit tests and benchmarks. Further details are given in Clients.
Sparse Linear Algebra Subroutines#
Each sparse linear algebra subroutine is implemented in a set of source files in the
corresponding directory: rocsparse_<subroutine>.cpp
, rocsparse_<subroutine>.hpp
and <subroutine>_device.h
,
where <subroutine> indicates any of the rocSPARSE library functions.
rocsparse_<subroutine>.cpp
implements the C wrapper and the API functionality for each precision supported.
Furthermore, rocsparse_<subroutine>.hpp
implements the API functionality, using the precision as template parameter.
Finally, <subroutine>_device.h
implements the device code, required for the computation of the subroutine.
Note
Each API exposed subroutine is expected to return a rocsparse_status
.
Additionally, each device function is expected to use a specified stream which is accessible through the libraries handle.
The following is a sample for rocsparse_<subroutine>.cpp
, rocsparse_<subroutine>.hpp
and <subroutine>_device.h
.
#include "rocsparse.h"
#include "rocsparse_subroutine.hpp"
/*
* ===========================
* C wrapper
* ===========================
*/
extern "C" rocsparse_status rocsparse_ssubroutine(rocsparse_handle handle,
rocsparse_int m,
const float* alpha,
float* val)
{
return rocsparse_subroutine_template(handle, m, alpha, val);
}
extern "C" rocsparse_status rocsparse_dsubroutine(rocsparse_handle handle,
rocsparse_int m,
const double* alpha,
double* val)
{
return rocsparse_subroutine_template(handle, m, alpha, val);
}
extern "C" rocsparse_status rocsparse_csubroutine(rocsparse_handle handle,
rocsparse_int m,
const rocsparse_float_complex* alpha,
rocsparse_float_complex* val)
{
return rocsparse_subroutine_template(handle, m, alpha, val);
}
extern "C" rocsparse_status rocsparse_zsubroutine(rocsparse_handle handle,
rocsparse_int m,
const rocsparse_double_complex* alpha,
rocsparse_double_complex* val)
{
return rocsparse_subroutine_template(handle, m, alpha, val);
}
#pragma once
#ifndef ROCSPARSE_SUBROUTINE_HPP
#define ROCSPARSE_SUBROUTINE_HPP
#include "definitions.h"
#include "handle.h"
#include "rocsparse.h"
#include "subroutine_device.h"
#include "utility.h"
#include <hip/hip_runtime.h>
template <typename T>
__global__ void subroutine_kernel_host_pointer(rocsparse_int m, T alpha, T* val)
{
subroutine_device(m, alpha, val);
}
template <typename T>
__global__ void subroutine_kernel_device_pointer(rocsparse_int m, const T* alpha, T* val)
{
subroutine_device(m, *alpha, val);
}
template <typename T>
rocsparse_status rocsparse_subroutine_template(rocsparse_handle handle,
rocsparse_int m,
const T* alpha,
T* val)
{
// Check for valid handle
if(handle == nullptr)
{
return rocsparse_status_invalid_handle;
}
// Logging
if(handle->pointer_mode == rocsparse_pointer_mode_host)
{
log_trace(handle,
rocsparse::replaceX<T>("rocsparse_Xsubroutine"),
m,
*alpha,
(const void*&)val);
log_bench(handle,
"./rocsparse-bench -f subroutine -r",
rocsparse::replaceX<T>("X"),
"-m",
m,
"--alpha",
*alpha);
}
else
{
log_trace(handle,
rocsparse::replaceX<T>("rocsparse_Xsubroutine"),
m,
(const void*&)alpha,
(const void*&)val);
}
// Check size
if(m < 0)
{
return rocsparse_status_invalid_size;
}
// Quick return if possible
if(m == 0)
{
return rocsparse_status_success;
}
// Check pointer arguments
if(alpha == nullptr || val == nullptr)
{
return rocsparse_status_invalid_pointer;
}
// Differentiate between the pointer modes
if(handle->pointer_mode == rocsparse_pointer_mode_device)
{
// Launch kernel
hipLaunchKernelGGL((subroutine_kernel_device_pointer<T>),
dim3(...),
dim3(...),
0,
handle->stream,
m,
alpha,
val);
}
else
{
// Launch kernel
hipLaunchKernelGGL((subroutine_kernel_host_pointer<T>),
dim3(...),
dim3(...),
0,
handle->stream,
m,
*alpha,
val);
}
return rocsparse_status_success;
}
#endif // ROCSPARSE_SUBROUTINE_HPP
#pragma once
#ifndef SUBROUTINE_DEVICE_H
#define SUBROUTINE_DEVICE_H
#include <hip/hip_runtime.h>
template <typename T>
__device__ void subroutine_device(rocsparse_int m, T alpha, T* val)
{
...
}
#endif // SUBROUTINE_DEVICE_H
Important Functions and Data Structures#
This section describes important rocSPARSE functions and data structures.
Status-Flag Macros#
The following table lists the status-flag macros available in rocSPARSE and their purpose.
Macro |
Description |
---|---|
|
Returns, if stat is not equal to |
|
Throws an exception, if stat is not equal to |
|
Prints an error message, if stat is not equal to |
|
Returns, if stat is not equal to |
The rocsparse_mat_info Structure#
The rocSPARSE rocsparse_mat_info
is a structure holding all matrix meta information that is gathered during analysis routines.
The following table lists all currently available internal metadata structures:
Meta data structure |
Description |
---|---|
|
Structure to hold analysis metadata for sparse matrix vector multiplication in CSR format. |
|
Structure to hold analysis metadata for operations on sparse triangular matrices, for example dependency graph. |
|
Structure to hold analysis metadata for sparse matrix sparse matrix multiplication in CSR format. |
Cross-Routine Data Sharing#
Already collected metadata, such as the dependency graph of a sparse matrix, can be shared among multiple routines. For example, if the incomplete LU factorization of a sparse matrix is computed, the gathered analysis data can be shared for subsequent lower triangular solves of the same matrix. This behavior can be specified by the rocsparse_analysis_policy parameter.
The following table lists subroutines that can in some cases share metadata:
Subroutine |
Sharing metadata with |
---|---|
Note
It is important to note, that on rocSPARSE extensions, this functionality can be further expanded to improve metadata collection performance significantly.
Clients#
rocSPARSE clients host a variety of different examples as well as a unit test and benchmarking package. For detailed instructions on how to build rocSPARSE with clients, see Installation.
Samples#
The clients/samples collection offers sample implementations of the rocSPARSE API. In the following table, available examples with description, are listed.
Sample |
Description |
---|---|
example_coomv |
Perform sparse matrix vector multiplication in COO format |
example_csrmv |
Perform sparse matrix vector multiplication in CSR format |
example_ellmv |
Perform sparse matrix vector multiplication in ELL format |
example_handle |
Show rocSPARSE handle initialization and finalization |
example_hybmv |
Perform sparse matrix vector multiplication in HYB format |
Unit Tests#
Multiple unit tests are available to test for bad arguments, invalid parameters and sparse routine functionality. The unit tests are based on GoogleTest. The tests cover all routines that are exposed by the API, including all available floating-point precision.
Benchmarks#
rocSPARSE offers a benchmarking tool that can be compiled with the clients package. The benchmark tool can perform any API exposed routine combined with time measurement. To set up a benchmark run, multiple options are available.
Command-line option |
Description |
---|---|
help, h |
Prints the help message |
sizem, m |
Specify the m parameter, e.g. the number of rows of a sparse matrix |
sizen, n |
Specify the n parameter, e.g. the number of columns of a sparse matrix or the length of a dense vector |
sizek, k |
Specify the k parameter, e.g. the number of rows of a dense matrix |
sizennz, z |
Specify the nnz parameter, e.g. the number of non-zero entries of a sparse vector |
blockdim |
Specify the blockdim parameter, e.g. the block dimension in BSR matrices |
row-blockdimA |
Specify the row-blockdimA parameter, e.g. the row block dimension in GEBSR matrices |
col-blockdimA |
Specify the col-blockdimA parameter, e.g. the column block dimension in GEBSR matrices |
row-blockdimB |
Specify the row-blockdimB parameter, e.g. the row block dimension in GEBSR matrices |
col-blockdimB |
Specify the col-blockdimB parameter, e.g. the column block dimension in GEBSR matrices |
mtx |
Read from MatrixMarket (.mtx) format. This will override parameters m, n and z |
rocalution |
Read from rocALUTION format. This will override parameters m, n, z, mtx and laplacian-dim |
laplacian-dim |
Assemble a 2D/3D Laplacian matrix with dimensions dimx, dimy and dimz. dimz is optional. This will override parameters m, n, z and mtx |
alpha |
Specify the scalar \(\alpha\) |
beta |
Specify the scalar \(\beta\) |
transposeA |
Specify whether matrix A is (conjugate) transposed or not, see rocsparse_operation |
transposeB |
Specify whether matrix B is (conjugate) transposed or not, see rocsparse_operation |
indexbaseA |
Specify the index base of matrix A, see rocsparse_index_base |
indexbaseB |
Specify the index base of matrix B, see rocsparse_index_base |
indexbaseC |
Specify the index base of matrix C, see rocsparse_index_base |
indexbaseD |
Specify the index base of matrix D, see rocsparse_index_base |
action |
Specify whether the operation is performed symbolically or numerically, see rocsparse_action |
hybpart |
Specify the HYB partitioning type, see rocsparse_hyb_partition |
diag |
Specify the diagonal type of a sparse matrix, see rocsparse_diag_type |
uplo |
Specify the fill mode of a sparse matrix, see rocsparse_fill_mode |
storage |
Specify the storage mode of a sparse matrix, see rocsparse_storage_mode |
apolicy |
Specify the analysis policy, see rocsparse_analysis_policy |
function, f |
Specify the API exposed subroutine to benchmark |
indextype |
Index precision: integer 32 bit, integer 64 bit |
precision, r |
Floating-point precision: single real, double real, single complex, double complex |
verify, v |
Specify whether the results should be validated with the host reference implementation |
iters, i |
Iterations to run inside the timing loop |
device, d |
Set the device to be used for subsequent benchmark runs |
direction |
Specify whether BSR blocks should be laid out in row-major storage or by column-major storage |
order |
Specify whether a dense matrix is laid out in column-major or row-major storage |
format |
Specify whether a sparse matrix is laid out in coo, coo_aos, csr, csc, or ell format |
denseld |
Specify the leading dimension of a dense matrix |
batch_count |
Specify the batch count for batched routines |
batch_count_A |
Specify the batch count for batched routines |
batch_count_B |
Specify the batch count for batched routines |
batch_count_C |
Specify the batch count for batched routines |
batch_stride |
Specify the batch stride for batched routines |
memstat-report |
Specify the output filename for memory report |
spmv_alg |
Specify the algorithm to use when running SpMV |
spmm_alg |
Specify the algorithm to use when running SpMM |
gtsv_interleaved_alg |
Specify the algorithm to use when running gtsv interleaved batch routine |
For example to benchmark the csrmv routine using double precision, you can run the following command:
`
./rocsparse-bench -f csrmv --precision d --alpha 1 --beta 0 --iters 1000 --rocalution <path to .csr matrix file>
`
Python plotting scripts#
rocSPARSE also contains some useful python plotting scripts that work in conjunction with the rocsparse-bench executable. To use these plotting scripts to, for example, plot the performance of csrmv routine with multiple matrices you would first call:
`
./rocsparse-bench -f csrmv --precision d --alpha 1 --beta 0 --iters 1000 --bench-x --rocalution /path/to/matrix/files/*.csr --bench-o name_of_output_file.json
`
This will produce the json file name_of_output_file.json
containing all the performance data. This file can then be passed to the python plotting script
rocSPARSE/scripts/rocsparse-bench-plot.py
like so:
`
python rocsparse-bench-plot.py /path/to/json/file/name_of_output_file.json
`
This will generate pdf files plotting:
GB/s
GFLOPS/s
milliseconds
Note
The parameter that comes after the --bench-x
option passed to rocsparse-bench
specifies which values will be used on the x-axis.
In the above example, we passed --rocalution /path/to/matrix/files/*.csr
following --bench-x
which means that each entry in the
x-axis of the generated plot will be a matrix found in the directory /path/to/matrix/files/
.
We also have plotting scripts that allow you to generate plots comparing two or more rocsparse-bench performance runs. For example if you want to compare the performance of csrmv with single precision and double precision, you would first run:
`
./rocsparse-bench -f csrmv --precision s --alpha 1 --beta 0 --iters 1000 --bench-x --rocalution /path/to/matrix/files/*.csr --bench-o scsrmv_output_file.json
`
`
./rocsparse-bench -f csrmv --precision d --alpha 1 --beta 0 --iters 1000 --bench-x --rocalution /path/to/matrix/files/*.csr --bench-o dcsrmv_output_file.json
`
Doing so generates the two json output files scsrmv_output_file.json and dcsrmv_output_file.json. These can then be passed to the python plotting script rocSPARSE/scripts/rocsparse-bench-compare.py like so:
`
python rocsparse-bench-compare.py /path/to/json/file/scsrmv_output_file.json /path/to/json/file/dcsrmv_output_file.json
`
This will generate pdf files plotting:
GB/s
GFLOPS/s
milliseconds
GB/s ratio
GFLOPS/s ratio
comparing the two runs.
In both python scripts, the y axis defaults to log scaling. If you would like linear scaling on the y axis you can pass the option –linear to either of the python plotting scripts. You can see a full list of options by using the -h|–help option.
Helper scripts for downloading matrices#
rocSPARSE contains some helper scripts for downloading matrices from the sparse suite collection. These matrices can be useful for additional testing and performance measurement. The scripts are found in rocSPARSE/scripts/performance/matrices. To use these scripts to download matrices, run the following commands:
`
./build_convert.sh
`
`
./get_matrices_1.sh
`
`
./get_matrices_2.sh
`
`
./get_matrices_3.sh
`
`
./get_matrices_4.sh
`
`
./get_matrices_5.sh
`
`
./get_matrices_6.sh
`
`
./get_matrices_7.sh
`
`
./get_matrices_8.sh
`
This will download the matrices and convert them to .csr format so that they can be used by rocsparse-bench using the –rocalution option.