Refactored repo

Manifest.toml

@@ -11,15 +11,15 @@ version = "0.10.9"
 
 [[Colors]]
 deps = ["ColorTypes", "FixedPointNumbers", "InteractiveUtils", "Reexport"]
-git-tree-sha1 = "008d6bc68dea6beb6303fdc37188cb557391ebf2"
+git-tree-sha1 = "ac5f2213e56ed8a34a3dd2f681f4df1166b34929"
 uuid = "5ae59095-9a9b-59fe-a467-6f913c188581"
-version = "0.12.4"
+version = "0.12.6"
 
 [[Conda]]
 deps = ["JSON", "VersionParsing"]
-git-tree-sha1 = "7a58bb32ce5d85f8bf7559aa7c2842f9aecf52fc"
+git-tree-sha1 = "c0647249d785f1d5139c0cc96db8f6b32f7ec416"
 uuid = "8f4d0f93-b110-5947-807f-2305c1781a2d"
-version = "1.4.1"
+version = "1.5.0"
 
 [[Dates]]
 deps = ["Printf"]
@@ -50,10 +50,6 @@ git-tree-sha1 = "c7aebfecb1a60d59c0fe023a68ec947a208b1e6b"
 uuid = "b964fa9f-0449-5b57-a5c2-d3ea65f4040f"
 version = "1.2.0"
 
-[[LibGit2]]
-deps = ["Printf"]
-uuid = "76f85450-5226-5b5a-8eaa-529ad045b433"
-
 [[Libdl]]
 uuid = "8f399da3-3557-5675-b5ff-fb832c97cbdb"
 
@@ -66,9 +62,9 @@ uuid = "56ddb016-857b-54e1-b83d-db4d58db5568"
 
 [[MacroTools]]
 deps = ["Markdown", "Random"]
-git-tree-sha1 = "f7d2e3f654af75f01ec49be82c231c382214223a"
+git-tree-sha1 = "6a8a2a625ab0dea913aba95c11370589e0239ff0"
 uuid = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09"
-version = "0.5.5"
+version = "0.5.6"
 
 [[Markdown]]
 deps = ["Base64"]
@@ -78,47 +74,35 @@ uuid = "d6f4376e-aef5-505a-96c1-9c027394607a"
 uuid = "a63ad114-7e13-5084-954f-fe012c677804"
 
 [[Parsers]]
-deps = ["Dates", "Test"]
-git-tree-sha1 = "8077624b3c450b15c087944363606a6ba12f925e"
+deps = ["Dates"]
+git-tree-sha1 = "50c9a9ed8c714945e01cd53a21007ed3865ed714"
 uuid = "69de0a69-1ddd-5017-9359-2bf0b02dc9f0"
-version = "1.0.10"
-
-[[Pkg]]
-deps = ["Dates", "LibGit2", "Libdl", "Logging", "Markdown", "Printf", "REPL", "Random", "SHA", "UUIDs"]
-uuid = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
+version = "1.0.15"
 
 [[Printf]]
 deps = ["Unicode"]
 uuid = "de0858da-6303-5e67-8744-51eddeeeb8d7"
 
 [[PyCall]]
 deps = ["Conda", "Dates", "Libdl", "LinearAlgebra", "MacroTools", "Serialization", "VersionParsing"]
-git-tree-sha1 = "b6dff5fa725eff4f775f472acd86756d6e31fb02"
+git-tree-sha1 = "dd1a970b543bd02efce2984582e996af28cab27f"
 uuid = "438e738f-606a-5dbb-bf0a-cddfbfd45ab0"
-version = "1.92.1"
+version = "1.92.2"
 
 [[PyPlot]]
 deps = ["Colors", "LaTeXStrings", "PyCall", "Sockets", "Test", "VersionParsing"]
 git-tree-sha1 = "67dde2482fe1a72ef62ed93f8c239f947638e5a2"
 uuid = "d330b81b-6aea-500a-939a-2ce795aea3ee"
 version = "2.9.0"
 
-[[REPL]]
-deps = ["InteractiveUtils", "Markdown", "Sockets"]
-uuid = "3fa0cd96-eef1-5676-8a61-b3b8758bbffb"
-
 [[Random]]
 deps = ["Serialization"]
 uuid = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 
 [[Reexport]]
-deps = ["Pkg"]
-git-tree-sha1 = "7b1d07f411bc8ddb7977ec7f377b97b158514fe0"
+git-tree-sha1 = "57d8440b0c7d98fc4f889e478e80f268d534c9d5"
 uuid = "189a3867-3050-52da-a836-e630ba90ab69"
-version = "0.2.0"
-
-[[SHA]]
-uuid = "ea8e919c-243c-51af-8825-aaa63cd721ce"
+version = "1.0.0"
 
 [[Serialization]]
 uuid = "9e88b42a-f829-5b0c-bbe9-9e923198166b"
@@ -138,10 +122,6 @@ uuid = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 deps = ["Distributed", "InteractiveUtils", "Logging", "Random"]
 uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
-[[UUIDs]]
-deps = ["Random", "SHA"]
-uuid = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"
-
 [[Unicode]]
 uuid = "4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"
 

Project.toml

@@ -1,3 +1,2 @@
 [deps]
 PyPlot = "d330b81b-6aea-500a-939a-2ce795aea3ee"
-Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"

README.md

@@ -1,42 +1,42 @@
-# PseudoTransientSolvers
-Concise and multi-[CPU/GPU]-ready iterative PDE solvers for Earth-science applications and beyond; examples.
+# ViscousGlacier2D
+Concise and CPU and GPU iterative Stokes solvers for viscous flow - 2D glacier examples.
 
 
 ## Description
-This repository contains various Pseudo-Transient (PT) routines with application to Earth-sciences (glaciology and subsurface processes). These routines exemplify the implementation of a fast and concise (60 lines of code) iterative solving strategy referred to as Pseudo-Transient. The core of the Pseudo-Transient approach relies in using physics-motivated transient terms within differential equations in order to iteratively converge to an accurate solution.
+This repository contains GPU and CPU implementations of a 2D Stokes solver to resolve glacier flow on an inclined plane with a stress-free surface. The routines exemplify the implementation of a fast and concise (60 lines of code) iterative solving strategy referred to as Pseudo-Transient. The core of the Pseudo-Transient approach relies in using physics-motivated transient terms within differential equations in order to iteratively converge to an accurate solution.
 
+The routines relate to a seminar on _"Simply solving PDEs"_. Slide compilation is accessible [here](#seminar-presentation).
 
 ## Content
 * [Script list](#script-list)
 * [Usage](#usage)
 * [Output](#output)
-* [To-Do](#to-do)
+* [Questions and comments](#questions-and-comments)
+* [Seminar presentation](#seminar-presentation)
 
 
 ## Script list
-The [/scripts](/scripts/) folder contains various PT routines grouped into subprojects. Currently, the subprojects and correpsonding routines are
-- [/scripts/viscous_gl2D](/scripts/viscous_gl2D/)
-  - `viscous_gl2D.jl`
-  - `viscous_gl2D_gpu.jl`
-  - `viscous_gl2D_nondim.jl`
-  - `viscous_gl2D_nondim_gpu.jl`
+The [/scripts](/scripts/) folder contains the following PT routines:
+- [`viscous_gl2D.jl`](scripts/viscous_gl2D.jl)
+- [`viscous_gl2D_gpu.jl`](scripts/viscous_gl2D_gpu.jl)
+- [`viscous_gl2D_nondim.jl`](scripts/viscous_gl2D_nondim.jl)
+- [`viscous_gl2D_nondim_gpu.jl`](scripts/viscous_gl2D_nondim_gpu.jl)
 
 The keywords in the code naming stand for:
-- viscous_gl2D: 2D viscous (full) Stokes with linear shear viscosity (scalar)
 - gpu: Nvidia GPU ready routines using the [CUDA.jl] package
 - nondim: non-dimensional version of the code, using natural scaling
 
 
 ## Usage
-If not stated otherwise, all the routines are written in Julia and can be executed from the REPL. Output is produced using [PyPlot.jl] requiring a valid and on `PATH` python (+ matplotlib) install.
+The routines are written in [Julia] and can be executed from the REPL. Output is produced using [PyPlot.jl] requiring a valid and on `PATH` python (+ matplotlib) install.
 
 Example running the `viscous_gl2D_nondim.jl` routine.
 
 1. Launch Julia
 ```sh
 % julia --project
 ```
-2. Activate and instantiate the environment
+2. ENter package mode `]` and instantiate the environment
 ```julia-repl
                _
    _       _ _(_)_     |  Documentation: https://docs.julialang.org
@@ -47,22 +47,21 @@ Example running the `viscous_gl2D_nondim.jl` routine.
  _/ |\__'_|_|_|\__'_|  |  Official https://julialang.org/ release
 |__/                   |
 
-julia> 
+julia>
 
-(PseudoTransientExamples) pkg> instantiate
+(ViscousGlacier2D) pkg> add instantiate
 
-(PseudoTransientExamples) pkg> st
-Status `~/Documents/git/github/PseudoTransientExamples/Project.toml`
+(ViscousGlacier2D) pkg> st
+Status `~/Documents/git/github/ViscousGlacier2D/Project.toml`
   [d330b81b] PyPlot v2.9.0
-  [10745b16] Statistics
 
-(PseudoTransientExamples) pkg> 
+(ViscousGlacier2D) pkg> 
 
 julia> 
 ```
 3. Run the script
 ```julia-repl
-julia> include("viscous_gl2D_nondim.jl")
+julia> include("scripts/viscous_gl2D_nondim.jl")
 error = 0.007844763087162187
 error = 0.0035814871757943543
 error = 0.0005235773871451197
@@ -83,15 +82,14 @@ julia>
 The output of running the `viscous_gl2D_nondim.jl` script is following
 ![Viscous 2D full Stokes flow (2D glacier) with stress free surface](docs/fig_viscous_gl2D_nondim.png)
 
+## Questions and comments
+Contact me or open an issue if you have any questions or comments about the presented material.
+
+## Seminar presentation
+
+![Simply solving PDEs](docs/slides.png)
 
-## To-Do
-A non-exhaustive list of potential future next steps:
-- complete the README
-  - with references and cross-refs
-  - further links to GPU-related computing, Julia-related computing, to stencil-based HPC Julia modules and distributed memory parallelisation Julia modules
-- add more code examples
-- add some simple visualisation scripts
-- certainly more to come... stay tuned
 
+[Julia]: https://julialang.org
 [CUDA.jl]: https://github.com/JuliaGPU/CUDA.jl
 [PyPlot.jl]: https://github.com/JuliaPy/PyPlot.jl

scripts/viscous_gl2D/viscous_gl2D.jl → scripts/viscous_gl2D.jl

@@ -53,10 +53,10 @@ using PyPlot, Statistics
 	st = 4; s2d = 60*60*24
 	(Xp,  Yp ) = (X2r[1:st:end,1:st:end], Y2r[1:st:end,1:st:end])
 	(Vxp, Vyp) = (0.5*(Vx[1:st:end-1,1:st:end  ]+Vx[2:st:end,1:st:end]), 0.5*(Vy[1:st:end  ,1:st:end-1]+Vy[1:st:end,2:st:end]))
-	subplot(311), pcolor(X2r,Y2r, 1e-3*Pt, shading="auto"), plt.axis("off"), plt.title("pressure [kPa]"), plt.colorbar(fraction=0.02, pad=0.03)
+	subplot(311), pcolor(X2r,Y2r, 1e-3*Pt), plt.axis("off"), plt.title("pressure [kPa]"), plt.colorbar(fraction=0.02, pad=0.03)
 	quiver(Xp, Yp, Vxp, Vyp, pivot="mid", color="white")
-	subplot(312), pcolor(X2r,Y2r, s2d*Vx[2:end,:], shading="auto"), plt.axis("off"), plt.title("Vel-x [m/d]"), plt.colorbar(fraction=0.02, pad=0.03)
-	subplot(313), pcolor(X2r,Y2r, s2d*Vy[:,2:end], shading="auto"), plt.axis("off"), plt.title("Vel-y [m/d]"), plt.colorbar(fraction=0.02, pad=0.03)
+	subplot(312), pcolor(X2r,Y2r, s2d*Vx[2:end,:]), plt.axis("off"), plt.title("Vel-x [m/d]"), plt.colorbar(fraction=0.02, pad=0.03)
+	subplot(313), pcolor(X2r,Y2r, s2d*Vy[:,2:end]), plt.axis("off"), plt.title("Vel-y [m/d]"), plt.colorbar(fraction=0.02, pad=0.03)
 	return
 end
 viscous_glacier2D()

scripts/viscous_gl2D/viscous_gl2D_gpu.jl → scripts/viscous_gl2D_gpu.jl

@@ -53,10 +53,10 @@ using CUDA, PyPlot, Statistics
 	st = 4; s2d = 60*60*24
 	(Xp,  Yp ) = (X2r[1:st:end,1:st:end], Y2r[1:st:end,1:st:end])
 	(Vxp, Vyp) = (0.5*(Vx[1:st:end-1,1:st:end  ]+Vx[2:st:end,1:st:end]), 0.5*(Vy[1:st:end  ,1:st:end-1]+Vy[1:st:end,2:st:end]))
-	subplot(311), pcolor(X2r,Y2r, 1e-3*Array(Pt), shading="auto"), plt.axis("off"), plt.title("pressure [kPa]"), plt.colorbar(fraction=0.02, pad=0.03)
+	subplot(311), pcolor(X2r,Y2r, 1e-3*Array(Pt)), plt.axis("off"), plt.title("pressure [kPa]"), plt.colorbar(fraction=0.02, pad=0.03)
 	quiver(Xp, Yp, Vxp, Vyp, pivot="mid", color="white")
-	subplot(312), pcolor(X2r,Y2r, s2d*Array(Vx[2:end,:]), shading="auto"), plt.axis("off"), plt.title("Vel-x [m/d]"), plt.colorbar(fraction=0.02, pad=0.03)
-	subplot(313), pcolor(X2r,Y2r, s2d*Array(Vy[:,2:end]), shading="auto"), plt.axis("off"), plt.title("Vel-y [m/d]"), plt.colorbar(fraction=0.02, pad=0.03)
+	subplot(312), pcolor(X2r,Y2r, s2d*Array(Vx[2:end,:])), plt.axis("off"), plt.title("Vel-x [m/d]"), plt.colorbar(fraction=0.02, pad=0.03)
+	subplot(313), pcolor(X2r,Y2r, s2d*Array(Vy[:,2:end])), plt.axis("off"), plt.title("Vel-y [m/d]"), plt.colorbar(fraction=0.02, pad=0.03)
 	return
 end
 viscous_glacier2D()

scripts/viscous_gl2D/viscous_gl2D_nondim.jl → scripts/viscous_gl2D_nondim.jl

@@ -59,10 +59,10 @@ using PyPlot, Statistics
 	st = 4; s2d = 60*60*24
 	(Xp,  Yp ) = (X2r[1:st:end,1:st:end], Y2r[1:st:end,1:st:end])
 	(Vxp, Vyp) = (0.5*(Vx[1:st:end-1,1:st:end  ]+Vx[2:st:end,1:st:end]), 0.5*(Vy[1:st:end  ,1:st:end-1]+Vy[1:st:end,2:st:end]))
-	subplot(311), pcolor(x̂*X2r,x̂*Y2r, 1e-3*p̂*Pt, shading="auto"), plt.axis("off"), plt.title("pressure [kPa]"), plt.colorbar(fraction=0.02, pad=0.03)
+	subplot(311), pcolor(x̂*X2r,x̂*Y2r, 1e-3*p̂*Pt), plt.axis("off"), plt.title("pressure [kPa]"), plt.colorbar(fraction=0.02, pad=0.03)
 	quiver(x̂*Xp, x̂*Yp, v̂*Vxp, v̂*Vyp, pivot="mid", color="white")
-	subplot(312), pcolor(x̂*X2r,x̂*Y2r, s2d*v̂*Vx[2:end,:], shading="auto"), plt.axis("off"), plt.title("Vel-x [m/d]"), plt.colorbar(fraction=0.02, pad=0.03)
-	subplot(313), pcolor(x̂*X2r,x̂*Y2r, s2d*v̂*Vy[:,2:end], shading="auto"), plt.axis("off"), plt.title("Vel-y [m/d]"), plt.colorbar(fraction=0.02, pad=0.03)
+	subplot(312), pcolor(x̂*X2r,x̂*Y2r, s2d*v̂*Vx[2:end,:]), plt.axis("off"), plt.title("Vel-x [m/d]"), plt.colorbar(fraction=0.02, pad=0.03)
+	subplot(313), pcolor(x̂*X2r,x̂*Y2r, s2d*v̂*Vy[:,2:end]), plt.axis("off"), plt.title("Vel-y [m/d]"), plt.colorbar(fraction=0.02, pad=0.03)
 	return
 end
 viscous_glacier2D()

scripts/viscous_gl2D/viscous_gl2D_nondim_gpu.jl → scripts/viscous_gl2D_nondim_gpu.jl

@@ -59,10 +59,10 @@ using CUDA, PyPlot, Statistics
 	st = 4; s2d = 60*60*24
 	(Xp,  Yp ) = (X2r[1:st:end,1:st:end], Y2r[1:st:end,1:st:end])
 	(Vxp, Vyp) = (0.5*(Vx[1:st:end-1,1:st:end  ]+Vx[2:st:end,1:st:end]), 0.5*(Vy[1:st:end  ,1:st:end-1]+Vy[1:st:end,2:st:end]))
-	subplot(311), pcolor(x̂*X2r,x̂*Y2r, 1e-3*p̂*Array(Pt), shading="auto"), plt.axis("off"), plt.title("pressure [kPa]"), plt.colorbar(fraction=0.02, pad=0.03)
+	subplot(311), pcolor(x̂*X2r,x̂*Y2r, 1e-3*p̂*Array(Pt)), plt.axis("off"), plt.title("pressure [kPa]"), plt.colorbar(fraction=0.02, pad=0.03)
 	quiver(x̂*Xp, x̂*Yp, v̂*Vxp, v̂*Vyp, pivot="mid", color="white")
-	subplot(312), pcolor(x̂*X2r,x̂*Y2r, s2d*v̂*Array(Vx[2:end,:]), shading="auto"), plt.axis("off"), plt.title("Vel-x [m/d]"), plt.colorbar(fraction=0.02, pad=0.03)
-	subplot(313), pcolor(x̂*X2r,x̂*Y2r, s2d*v̂*Array(Vy[:,2:end]), shading="auto"), plt.axis("off"), plt.title("Vel-y [m/d]"), plt.colorbar(fraction=0.02, pad=0.03)
+	subplot(312), pcolor(x̂*X2r,x̂*Y2r, s2d*v̂*Array(Vx[2:end,:])), plt.axis("off"), plt.title("Vel-x [m/d]"), plt.colorbar(fraction=0.02, pad=0.03)
+	subplot(313), pcolor(x̂*X2r,x̂*Y2r, s2d*v̂*Array(Vy[:,2:end])), plt.axis("off"), plt.title("Vel-y [m/d]"), plt.colorbar(fraction=0.02, pad=0.03)
 	return
 end
 viscous_glacier2D()