Merge pull request #43 from SciML/gpu

Update GPU syntax to allow for backend choices

CRAN-SUBMISSION

@@ -1,3 +1,3 @@
-Version: 1.1.3
-Date: 2022-12-24 06:05:19 UTC
-SHA: 4bb05b0a4dd334ff96db8db1a4fbf5b8f61a3db7
+Version: 2.0.0
+Date: 2023-10-20 04:03:09 UTC
+SHA: 0db73d96940765d14af0154b85d419edc5b71b55

DESCRIPTION

@@ -1,12 +1,13 @@
 Package: diffeqr
 Type: Package
 Title: Solving Differential Equations (ODEs, SDEs, DDEs, DAEs)
-Version: 1.1.3
+Version: 2.0.0
 Authors@R: person("Christopher", "Rackauckas", email = "[email protected]", role = c("aut", "cre", "cph"))
 Description: An interface to 'DifferentialEquations.jl' <https://diffeq.sciml.ai/dev/> from the R programming language.
   It has unique high performance methods for solving ordinary differential equations (ODE), stochastic differential equations (SDE),
   delay differential equations (DDE), differential-algebraic equations (DAE), and more. Much of the functionality,
   including features like adaptive time stepping in SDEs, are unique and allow for multiple orders of magnitude speedup over more common methods.
+  Supports GPUs, with support for CUDA (NVIDIA), AMD GPUs, Intel oneAPI GPUs, and Apple's Metal (M-series chip GPUs).
   'diffeqr' attaches an R interface onto the package, allowing seamless use of this tooling by R users. For more information,
   see Rackauckas and Nie (2017) <doi:10.5334/jors.151>.
 Depends: R (>= 3.4.0)

NEWS.md

@@ -1,3 +1,8 @@
+## Release v2.0.0
+
+Support new DiffEqGPU syntax. This requires passing a backend. Supports NVIDIA CUDA, Intel OneAPI,
+AMD GPUs, and Apple Metal GPUs. Also much faster GPU compilation and runtime performance.
+
 ## Release v1.1.2
 
 Bugfixes for newer Julia versions.

R/diffeqr.R

@@ -117,20 +117,47 @@ jitoptimize_sde <- function (de,prob){
 #' This function initializes the DiffEqGPU package for GPU-parallelized ensembles.
 #' The first time will be long since it includes precompilation.
 #'
+#' @param backend the backend for the GPU computation. Choices are "CUDA", "AMDGPU", "Metal", or "oneAPI"
+#'
 #' @examples
 #'
 #' \dontrun{ ## diffeq_setup() is time-consuming and requires Julia+DifferentialEquations.jl
 #'
-#' degpu <- diffeqr::diffeqgpu_setup()
+#' degpu <- diffeqr::diffeqgpu_setup(backend="CUDA")
 #'
 #' }
 #'
 #' @export
-diffeqgpu_setup <- function (){
+diffeqgpu_setup <- function (backend){
   JuliaCall::julia_install_package_if_needed("DiffEqGPU")
   JuliaCall::julia_library("DiffEqGPU")
   functions <- JuliaCall::julia_eval("filter(isascii, replace.(string.(propertynames(DiffEqGPU)),\"!\"=>\"_bang\"))")
   degpu <- julia_pkg_import("DiffEqGPU",functions)
+
+  if (backend == "CUDA") {
+    JuliaCall::julia_install_package_if_needed("CUDA")
+    JuliaCall::julia_library("CUDA")
+    backend <- julia_pkg_import("CUDA",c("CUDABackend"))
+    degpu$CUDABackend <- backend$CUDABackend
+  } else if (backend == "AMDGPU") {
+    JuliaCall::julia_install_package_if_needed("AMDGPU")
+    JuliaCall::julia_library("AMDGPU")
+    backend <- julia_pkg_import("AMDGPU",c("AMDGPUBackend"))
+    degpu$AMDGPUBackend <- backend$AMDGPUBackend
+  } else if (backend == "Metal") {
+    JuliaCall::julia_install_package_if_needed("Metal")
+    JuliaCall::julia_library("Metal")
+    backend <- julia_pkg_import("Metal",c("MetalBackend"))
+    degpu$MetalBackend <- backend$MetalBackend
+  } else if (backend == "oneAPI") {
+    JuliaCall::julia_install_package_if_needed("oneAPI")
+    JuliaCall::julia_library("oneAPI")
+    backend <- julia_pkg_import("oneAPI",c("oneAPIBackend"))
+    degpu$oneAPIBackend <- backend$oneAPIBackend
+  } else {
+    stop(paste("Illegal backend choice found. Allowed choices: CUDA, AMDGPU, Metal, and oneAPI. Chosen backend: ", backend))
+  }
+  degpu
 }
 
 julia_function <- function(func_name, pkg_name = "Main",