spmv#
Computes a sparse matrix by dense vector product.
Description and Assumptions
The oneapi::mkl::sparse::spmv
routine computes a sparse matrix by dense
vector product defined as:
n
,m
,m
-by-n
,spmv_descr#
Definition
namespace oneapi::mkl::sparse {
struct spmv_descr;
using spmv_descr_t = spmv_descr*;
}
Description
Defines spmv_descr_t
as an opaque pointer to the incomplete type
spmv_descr
. Each backend may provide a different implementation of the
type spmv_descr
. The spmv_descr_t
object persists through the various
stages of the spmv operation to house relevant state, optimizations and
workspaces.
init_spmv_descr#
Syntax
namespace oneapi::mkl::sparse {
void init_spmv_descr (sycl::queue &queue,
oneapi::mkl::sparse::spmv_descr_t *p_spmv_descr);
}
Input parameters
- queue
The SYCL command queue which will be used for SYCL kernels execution.
- p_spmv_descr
The address of the
p_spmv_descr
object to be initialized. Must only be called on an uninitializedspmv_descr_t
object.
Output parameters
- p_spmv_descr
On return, the address is updated to point to a newly allocated and initialized
spmv_descr_t
object that can be used to perform spmv.
Throws
This function shall throw the following exceptions if the associated condition is detected. An implementation may throw additional implementation-specific exception(s) in case of error conditions not covered here.
release_spmv_descr#
Syntax
namespace oneapi::mkl::sparse {
sycl::event release_spmv_descr (sycl::queue &queue,
oneapi::mkl::sparse::spmv_descr_t spmv_descr,
const std::vector<sycl::event> &dependencies = {});
}
Input parameters
- queue
The SYCL command queue which will be used for SYCL kernels execution.
- spmv_descr
Descriptor initialized with
init_spmv_descr
.- dependencies
List of events to depend on before starting asynchronous tasks that access data on the device. Defaults to no dependencies.
Return Values
Output event that can be waited upon or added as a dependency for the completion of the function.
Throws
This function shall throw the following exceptions if the associated condition is detected. An implementation may throw additional implementation-specific exception(s) in case of error conditions not covered here.
spmv_alg#
Syntax
namespace oneapi::mkl::sparse {
enum class spmv_alg {
default_alg,
no_optimize_alg,
coo_alg1,
coo_alg2,
csr_alg1,
csr_alg2,
csr_alg3,
};
}
Description
These algorithm enums are provided in case backends would like to implement various different algorithms for the operation. Behavior of the algorithms (e.g., bitwise reproducibility, atomics usage) and the preconditions to using specific algorithms (e.g. sortedness of matrix arrays) is implementation-defined and must be documented in the library implementing the oneAPI specification.
spmv#
Syntax
namespace oneapi::mkl::sparse {
void spmv_buffer_size(
sycl::queue &queue,
oneapi::mkl::transpose opA,
const void* alpha,
oneapi::mkl::sparse::matrix_view A_view,
oneapi::mkl::sparse::matrix_handle_t A_handle,
oneapi::mkl::sparse::dense_vector_handle_t x_handle,
const void* beta,
oneapi::mkl::sparse::dense_vector_handle_t y_handle,
oneapi::mkl::sparse::spmv_alg alg,
oneapi::mkl::sparse::spmv_descr_t spmv_descr,
std::size_t &temp_buffer_size);
void spmv_optimize(
sycl::queue &queue,
oneapi::mkl::transpose opA,
const void* alpha,
oneapi::mkl::sparse::matrix_view A_view,
oneapi::mkl::sparse::matrix_handle_t A_handle,
oneapi::mkl::sparse::dense_vector_handle_t x_handle,
const void* beta,
oneapi::mkl::sparse::dense_vector_handle_t y_handle,
oneapi::mkl::sparse::spmv_alg alg,
oneapi::mkl::sparse::spmv_descr_t spmv_descr,
sycl::buffer<std::uint8_t, 1> workspace);
sycl::event spmv_optimize(
sycl::queue &queue,
oneapi::mkl::transpose opA,
const void* alpha,
oneapi::mkl::sparse::matrix_view A_view,
oneapi::mkl::sparse::matrix_handle_t A_handle,
oneapi::mkl::sparse::dense_vector_handle_t x_handle,
const void* beta,
oneapi::mkl::sparse::dense_vector_handle_t y_handle,
oneapi::mkl::sparse::spmv_alg alg,
oneapi::mkl::sparse::spmv_descr_t spmv_descr,
void* workspace,
const std::vector<sycl::event> &dependencies = {});
sycl::event spmv(
sycl::queue &queue,
oneapi::mkl::transpose opA,
const void* alpha,
oneapi::mkl::sparse::matrix_view A_view,
oneapi::mkl::sparse::matrix_handle_t A_handle,
oneapi::mkl::sparse::dense_vector_handle_t x_handle,
const void* beta,
oneapi::mkl::sparse::dense_vector_handle_t y_handle,
oneapi::mkl::sparse::spmv_alg alg,
oneapi::mkl::sparse::spmv_descr_t spmv_descr,
const std::vector<sycl::event> &dependencies = {});
}
Notes
spmv_buffer_size
andspmv_optimize
must be called at least once beforespmv
with the same arguments.spmv
can then be called multiple times. Callingspmv_optimize
on the same descriptor can reset some of the descriptor’s data such as theworkspace
.In the general case, not calling the functions in the order specified above is undefined behavior. Not calling
spmv_buffer_size
orspmv_optimize
at least once with a given descriptor will throw an oneapi::mkl::uninitialized exception. Callingspmv
with arguments not matchingspmv_optimize
will throw an oneapi::mkl::invalid_argument exception, unless stated otherwise.The data of the dense handles
x_handle
andy_handle
and the scalarsalpha
andbeta
can be reset before each call tospmv
. Changing the data of the sparse handleA_handle
is undefined behavior.The data must be available on the device when calling
spmv_optimize
by using event dependencies if needed.spmv_optimize
andspmv
are asynchronous.The algorithm defaults to
spmv_alg::default_alg
if a backend does not support the provided algorithm.The container type of all the handles and
workspace
must be consistent and use either USM pointers or SYCL buffers.
Input Parameters
- queue
The SYCL command queue which will be used for SYCL kernels execution.
- opA
Specifies operation
op()
on the input matrix. The possible options are described in transpose enum class.- alpha
Host or USM pointer representing \(\alpha\). The USM allocation can be on the host or device. The requirements are:
Must use the same kind of memory as
beta
.Must be a host pointer if SYCL buffers are used.
Must be of the same type as the handles’ data type.
- A_view
Specifies which part of the handle should be read as described by matrix_view. The
type_view
field cannot bematrix_descr::diagonal
. Thediag_view
field can bediag::unit
if and only iftype_view
ismatrix_descr::triangular
.- A_handle
Sparse matrix handle object representing \(A\).
- x_handle
Dense vector handle object representing \(x\).
- beta
Host or USM pointer representing \(\beta\). The USM allocation can be on the host or device. The requirements are:
Must use the same kind of memory as
alpha
.Must be a host pointer if SYCL buffers are used.
Must be of the same type as the handles’ data type.
- y_handle
Dense vector handle object representing \(y\).
- alg
Specifies the spmv algorithm to use.
- spmv_descr
Initialized spmv descriptor.
- temp_buffer_size
Output buffer size in bytes.
- workspace
- Workspace buffer or USM pointer, must be at least of size
temp_buffer_size
bytes and the address aligned on the size of the handles’ data type.If it is a buffer, its lifetime is extended until the spmv descriptor is released or the workspace is reset byspmv_optimize
. The workspace cannot be a sub-buffer.If it is a USM pointer, it must not be free’d until the correspondingspmv
has completed. The data must be accessible on the device. - dependencies
List of events to depend on before starting asynchronous tasks that access data on the device. Ignored if buffers are used. Defaults to no dependencies.
Output Parameters
- temp_buffer_size
Output buffer size in bytes. A temporary workspace of at least this size must be allocated to perform the specified spmv.
- y_handle
Dense vector handle object representing \(y\), result of the
spmv
operation.
Return Values
Output event that can be waited upon or added as a dependency for the completion of the function. May be an empty event if buffers are used.
Throws
These functions shall throw the following exceptions if the associated condition is detected. An implementation may throw additional implementation-specific exception(s) in case of error conditions not covered here.
Parent topic: Sparse BLAS