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Moved explanations and comments to the beginning of astaroth.cu. No code changes.

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jpekkila
2019-07-05 15:39:52 +03:00
parent d87eb36f5a
commit ce8fe53f91
1 changed files with 123 additions and 113 deletions
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src/core/astaroth.cu
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@@ -21,7 +21,106 @@
* @file
* \brief Multi-GPU implementation.
*
* Detailed info.
%JP: The old way for computing boundary conditions conflicts with the
way we have to do things with multiple GPUs.
The older approach relied on unified memory, which represented the whole
memory area as one huge mesh instead of several smaller ones. However, unified memory
in its current state is more meant for quick prototyping when performance is not an issue.
Getting the CUDA driver to migrate data intelligently across GPUs is much more difficult
than when managing the memory explicitly.
In this new approach, I have simplified the multi- and single-GPU layers significantly.
Quick rundown:
New struct: Grid. There are two global variables, "grid" and "subgrid", which
contain the extents of the whole simulation domain and the decomposed grids,
respectively. To simplify thing, we require that each GPU is assigned the same amount of
work, therefore each GPU in the node is assigned and "subgrid.m" -sized block of data to
work with.
The whole simulation domain is decomposed with respect to the z dimension.
For example, if the grid contains (nx, ny, nz) vertices, then the subgrids
contain (nx, ny, nz / num_devices) vertices.
An local index (i, j, k) in some subgrid can be mapped to the global grid with
global idx = (i, j, k + device_id * subgrid.n.z)
Terminology:
- Single-GPU function: a function defined on the single-GPU layer (device.cu)
Changes required to this commented code block:
- The thread block dimensions (tpb) are no longer passed to the kernel here but in
device.cu instead. Same holds for any complex index calculations. Instead, the local
coordinates should be passed as an int3 type without having to consider how the data is
actually laid out in device memory
- The unified memory buffer no longer exists (d_buffer). Instead, we have an opaque
handle of type "Device" which should be passed to single-GPU functions. In this file, all
devices are stored in a global array "devices[num_devices]".
- Every single-GPU function is executed asynchronously by default such that we
can optimize Astaroth by executing memory transactions concurrently with
computation. Therefore a StreamType should be passed as a parameter to single-GPU functions.
Refresher: CUDA function calls are non-blocking when a stream is explicitly passed
as a parameter and commands executing in different streams can be processed
in parallel/concurrently.
Note on periodic boundaries (might be helpful when implementing other boundary conditions):
With multiple GPUs, periodic boundary conditions applied on indices ranging from
(0, 0, STENCIL_ORDER/2) to (subgrid.m.x, subgrid.m.y, subgrid.m.z -
STENCIL_ORDER/2)
on a single device are "local", in the sense that they can be computed without
having to exchange data with neighboring GPUs. Special care is needed only for transferring
the data to the fron and back plates outside this range. In the solution we use
here, we solve the local boundaries first, and then just exchange the front and back plates
in a "ring", like so
device_id
(n) <-> 0 <-> 1 <-> ... <-> n <-> (0)
### Throughout this file we use the following notation and names for various index offsets
Global coordinates: coordinates with respect to the global grid (static Grid grid)
Local coordinates: coordinates with respect to the local subgrid (static Subgrid subgrid)
s0, s1: source indices in global coordinates
d0, d1: destination indices in global coordinates
da = max(s0, d0);
db = min(s1, d1);
These are used in at least
acLoad()
acStore()
acSynchronizeHalos()
Here we decompose the host mesh and distribute it among the GPUs in
the node.
The host mesh is a huge contiguous block of data. Its dimensions are given by
the global variable named "grid". A "grid" is decomposed into "subgrids",
one for each GPU. Here we check which parts of the range s0...s1 maps
to the memory space stored by some GPU, ranging d0...d1, and transfer
the data if needed.
The index mapping is inherently quite involved, but here's a picture which
hopefully helps make sense out of all this.
Grid
|----num_vertices---|
xxx|....................................................|xxx
^ ^ ^ ^
d0 d1 s0 (src) s1
Subgrid
xxx|.............|xxx
^ ^
d0 d1
^ ^
db da
*
*/
#include "astaroth.h"
@@ -151,36 +250,7 @@ acQuit(void)
AcResult
acLoadWithOffset(const AcMesh& host_mesh, const int3& src, const int num_vertices)
{
/*
Here we decompose the host mesh and distribute it among the GPUs in
the node.
The host mesh is a huge contiguous block of data. Its dimensions are given by
the global variable named "grid". A "grid" is decomposed into "subgrids",
one for each GPU. Here we check which parts of the range s0...s1 maps
to the memory space stored by some GPU, ranging d0...d1, and transfer
the data if needed.
The index mapping is inherently quite involved, but here's a picture which
hopefully helps make sense out of all this.
Grid
|----num_vertices---|
xxx|....................................................|xxx
^ ^ ^ ^
d0 d1 s0 (src) s1
Subgrid
xxx|.............|xxx
^ ^
d0 d1
^ ^
db da
*/
// See the beginning of the file for an explanation of the index mapping
for (int i = 0; i < num_devices; ++i) {
const int3 d0 = (int3){0, 0, i * subgrid.n.z}; // DECOMPOSITION OFFSET HERE
const int3 d1 = (int3){subgrid.m.x, subgrid.m.y, d0.z + subgrid.m.z};
@@ -216,7 +286,7 @@ acLoadWithOffset(const AcMesh& host_mesh, const int3& src, const int num_vertice
AcResult
acStoreWithOffset(const int3& src, const int num_vertices, AcMesh* host_mesh)
{
// See acLoadWithOffset() for an explanation of the index mapping
// See the beginning of the file for an explanation of the index mapping
for (int i = 0; i < num_devices; ++i) {
const int3 d0 = (int3){0, 0, i * subgrid.n.z}; // DECOMPOSITION OFFSET HERE
const int3 d1 = (int3){subgrid.m.x, subgrid.m.y, d0.z + subgrid.m.z};
@@ -270,6 +340,9 @@ acSynchronizeHalos(void)
// We loop only to num_devices - 1 since the front and back plate of the grid is not
// transferred because their contents depend on the boundary conditions.
// IMPORTANT NOTE: the boundary conditions must be applied before calling this function!
// I.e. the halos of subgrids must contain up-to-date data!
for (int i = 0; i < num_devices - 1; ++i) {
const int num_vertices = subgrid.m.x * subgrid.m.y * NGHOST;
// ...|ooooxxx|... -> xxx|ooooooo|...
@@ -324,70 +397,24 @@ acBoundcondStep(void)
const int3 d1 = (int3){subgrid.m.x, subgrid.m.y, d0.z + subgrid.n.z};
boundcondStep(devices[i], STREAM_PRIMARY, d0, d1);
}
// With periodic boundary conditions we exchange the front and back plates of the
// grid. The exchange is done between the first and last device (0 and num_devices - 1).
const int num_vertices = subgrid.m.x * subgrid.m.y * NGHOST;
// ...|ooooxxx|... -> xxx|ooooooo|...
{
const int3 src = (int3){0, 0, subgrid.n.z};
const int3 dst = (int3){0, 0, 0};
copyMeshDeviceToDevice(devices[num_devices - 1], STREAM_PRIMARY, src, devices[0], dst,
num_vertices);
}
// ...|ooooooo|xxx <- ...|xxxoooo|...
{
const int3 src = (int3){0, 0, NGHOST};
const int3 dst = (int3){0, 0, NGHOST + subgrid.n.z};
copyMeshDeviceToDevice(devices[0], STREAM_PRIMARY, src, devices[num_devices - 1], dst,
num_vertices);
}
/*
// ===MIIKKANOTE START==========================================
%JP: The old way for computing boundary conditions conflicts with the
way we have to do things with multiple GPUs.
The older approach relied on unified memory, which represented the whole
memory area as one huge mesh instead of several smaller ones. However, unified memory
in its current state is more meant for quick prototyping when performance is not an issue.
Getting the CUDA driver to migrate data intelligently across GPUs is much more difficult
than when managing the memory explicitly.
In this new approach, I have simplified the multi- and single-GPU layers significantly.
Quick rundown:
New struct: Grid. There are two global variables, "grid" and "subgrid", which
contain the extents of the whole simulation domain and the decomposed grids,
respectively. To simplify thing, we require that each GPU is assigned the same amount of
work, therefore each GPU in the node is assigned and "subgrid.m" -sized block of data to
work with.
The whole simulation domain is decomposed with respect to the z dimension.
For example, if the grid contains (nx, ny, nz) vertices, then the subgrids
contain (nx, ny, nz / num_devices) vertices.
An local index (i, j, k) in some subgrid can be mapped to the global grid with
global idx = (i, j, k + device_id * subgrid.n.z)
Terminology:
- Single-GPU function: a function defined on the single-GPU layer (device.cu)
Changes required to this commented code block:
- The thread block dimensions (tpb) are no longer passed to the kernel here but in
device.cu instead. Same holds for any complex index calculations. Instead, the local
coordinates should be passed as an int3 type without having to consider how the data is
actually laid out in device memory
- The unified memory buffer no longer exists (d_buffer). Instead, we have an opaque
handle of type "Device" which should be passed to single-GPU functions. In this file, all
devices are stored in a global array "devices[num_devices]".
- Every single-GPU function is executed asynchronously by default such that we
can optimize Astaroth by executing memory transactions concurrently with
computation. Therefore a StreamType should be passed as a parameter to single-GPU functions.
Refresher: CUDA function calls are non-blocking when a stream is explicitly passed
as a parameter and commands executing in different streams can be processed
in parallel/concurrently.
Note on periodic boundaries (might be helpful when implementing other boundary conditions):
With multiple GPUs, periodic boundary conditions applied on indices ranging from
(0, 0, STENCIL_ORDER/2) to (subgrid.m.x, subgrid.m.y, subgrid.m.z -
STENCIL_ORDER/2)
on a single device are "local", in the sense that they can be computed without
having to exchange data with neighboring GPUs. Special care is needed only for transferring
the data to the fron and back plates outside this range. In the solution we use
here, we solve the local boundaries first, and then just exchange the front and back plates
in a "ring", like so
device_id
(n) <-> 0 <-> 1 <-> ... <-> n <-> (0)
// ======MIIKKANOTE END==========================================
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< MIIKKANOTE: This code block was essentially
moved into device.cu, function
boundCondStep() In astaroth.cu, we use acBoundcondStep() just to distribute the work and
@@ -417,23 +444,6 @@ acBoundcondStep(void)
periodic_boundconds(0, tpb, start, end, d_buffer.in[i]);
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
*/
// With periodic boundary conditions we exchange the front and back plates of the
// grid. The exchange is done between the first and last device (0 and num_devices - 1).
const int num_vertices = subgrid.m.x * subgrid.m.y * NGHOST;
// ...|ooooxxx|... -> xxx|ooooooo|...
{
const int3 src = (int3){0, 0, subgrid.n.z};
const int3 dst = (int3){0, 0, 0};
copyMeshDeviceToDevice(devices[num_devices - 1], STREAM_PRIMARY, src, devices[0], dst,
num_vertices);
}
// ...|ooooooo|xxx <- ...|xxxoooo|...
{
const int3 src = (int3){0, 0, NGHOST};
const int3 dst = (int3){0, 0, NGHOST + subgrid.n.z};
copyMeshDeviceToDevice(devices[0], STREAM_PRIMARY, src, devices[num_devices - 1], dst,
num_vertices);
}
}
acSynchronize();
return AC_SUCCESS;
@@ -442,9 +452,9 @@ acBoundcondStep(void)
AcResult
acIntegrateStepWithOffset(const int& isubstep, const AcReal& dt, const int3& start, const int3& end)
{
// See the beginning of the file for an explanation of the index mapping
for (int i = 0; i < num_devices; ++i) {
// DECOMPOSITION OFFSET HERE
// Same naming here (d0, d1, da, db) as in acLoadWithOffset
const int3 d0 = (int3){NGHOST, NGHOST, NGHOST + i * subgrid.n.z};
const int3 d1 = d0 + (int3){subgrid.n.x, subgrid.n.y, subgrid.n.z};