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AMGX.jl

AMGX in Julia

Build Status

The AMGX.jl package provides an interface for using NVIDIA's AMGX library from the Julia language.

Installation

The package is installed using the Julia package manager:

using Pkg; Pkg.add("AMGX")

Currently, only prebuilt binaries are available for Linux, on other operating systems you need to have AMGX available locally.

Before using the package, reading through the official API reference docs for AMGX is recommended.

API

Initialization

If you do not want to use the provided prebuilt binaries, set the environment variable JULIA_AMGX_PATH to the path to the local AMGX library.

The library can now be initialized with:

using AMGX
AMGX.initialize()

Config

An AMGX Config can be created from a dictionary or a string:

config = AMGX.Config(Dict("monitor_residual" => 1, "max_iters" => 10, "store_res_history" => 1));

Resources

An AMGX Resources object is created from an AMGX Config. Currently, only simple resources are wrapped:

resources = AMGX.Resources(config)

Mode

The different modes in AMGX are available as:

  • AMGX.hDDI
  • AMGX.hDFI
  • AMGX.hFFI
  • AMGX.dDDI
  • AMGX.dDFI
  • AMGX.dFFI

Vector

An AMGXVector is created from a resource object with a given mode.

v = AMGX.AMGXVector(resources, AMGX.dDDI)

Data can then be uploaded to the vectorusing upload!:

AMGX.upload!(v, [1.0, 2.0, 3.0])

Optionally, the "block dimension" can be given:

v_block = AMGX.AMGXVector(resources, AMGX.dDDI)
AMGX.upload!(v_block, [1.0, 2.0, 3.0, 4.0]; block_dim=2)

Data can be downloaded from the vector (for example after solving a system) using Vector:

v_h = Vector(v)

It is also possible to download to a preallocated buffer using copy!:

v_h_buffer = zeros(3)
copy!(v_h_buffer, v)

Note that data can be uploaded / downloaded from arrays already allocated on the GPU (CuArray):

using CUDA
v_cu = AMGX.AMGXVector(resources, AMGX.dDDI)
cu = CuVector([1.0, 2.0, 3.0])
AMGX.upload!(v_cu, cu)
cu_buffer = CUDA.zeros(Float64, 3)
copy!(cu_buffer, v_cu)

A vector can be set to zero:

v_zero = AMGX.AMGXVector(resources, AMGX.dDDI)
AMGX.set_zero!(v_zero, 5)

AMGXMatrix

Matrices in AMGX are stored in CSR format (as opposed to CSC which is typically used in Julia). An AMGXMatrix is created from a Resources and a mode:

matrix = AMGX.AMGXMatrix(resources, AMGX.dDDI)

Data can be uploaded to it using the 3 CSR arrays:

AMGX.upload!(matrix, 
    Cint[0, 1, 3], # row_ptrs
    Cint[1, 0, 1], # col_indices
    [1.0, 2.0, 3.0] # data
)

These arrays can also be uploaded from CuArrays already residing on the GPU.

Alternatively, a CUDA.CUSPARSE.CuSparseMatrixCSR can be directly uploaded.

The non zero values can be replaced:

AMGX.replace_coefficients!(matrix, [3.0, 2.0, 1.0])

Solver

A solver is created from a Resources, a Mode, and a Config:

solver = AMGX.Solver(resources, AMGX.dDDI, config)

A system can now be solved as:

x = AMGX.AMGXVector(resources, AMGX.dDDI)
AMGX.set_zero!(x, 3)
AMGX.setup!(solver, matrix)
AMGX.solve!(x, solver, v)

The solution vector can now be downloaded:

Vector(x)

After a solve, the status can be retrieved using AMGX.get_status(solver). It is of type AMGX.SolverStatus and can be either:

  • AMGX.SUCCESS
  • AMGX.FAILED
  • AMGX.DIVERGED
  • AMGX.NOT_CONVERGED

The total number of iterations can be retrieved with AMGX.get_iterations_number(solver).

The residual for a given iteration can be retrieved with

AMGX.get_iteration_residual(solver)
AMGX.get_iteration_residual(solver, 0)

Utilities

Version information

The API version is retrieved with AMGX.api_version(). Some more version info can be printed using AMGX.versioninfo().

Pinning/Unpinning memory

For performance, it is recommended to pin host memory before uploading it to the GPU. Pinning and unpinning of memory is done using:

v = rand(5)
AMGX.pin_memory(v)
AMGX.unpin_memory(v)

Print callback

By default, the AMGX library prints various things to stdout. This can be overridden by registering a print callback, which is a Julia function accepting a String and returning nothing:

str = ""
store_to_str(amgx_printed::String) = (global str = amgx_printed; nothing)
AMGX.register_print_callback(store_to_str)
c_config = AMGX.Config("")
print(str)
print_stdout(amgx_printed::String) = print(stdout, amgx_printed)
AMGX.register_print_callback(print_stdout)

Signal handlers

Signal handlers can be installed and reset using:

AMGX.install_signal_handler()
AMGX.reset_signal_handler()

Memory management and finalizing

You need to explicitly free memory of every AMGX object (Config, Resources, AMGXVector, AMGXMatrix, Solver) created using the julia call close. Using Defer.jl can significantly increase the convenience of this.

AMGX.jl contains a reference counting system so that it errors if you try to close things in the wrong order (e.g. closing the Resources object before the AMGXVector created from it is closed (destroyed))

When usage of the library is done, it should be finalized with:

AMGX.finalize_plugins()
AMGX.finalize()

Not implemented:

The following functions from the C-API are not yet implemented:

  • AMGX_read_system
  • AMGX_read_system_distributed
  • AMGX_write_system
  • AMGX_write_system_distributed
  • AMGX_config_create_from_file
  • AMGX_config_get_default_number_of_rings
  • AMGX_resources_create (only simple is currently wrapped)
  • AMGX_matrix_upload_all_global
  • AMGX_matrix_comm_from_maps
  • AMGX_matrix_comm_from_maps_one_ring
  • AMGX_vector_bind