cwpearson/astaroth
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Added Astaroth 2.0

Browse Source
This commit is contained in:
jpekkila
2019-06-14 14:18:35 +03:00
parent 4e4f84c8ff
commit 0e48766a68
87 changed files with 18058 additions and 1 deletions
Download Patch File Download Diff File Expand all files Collapse all files

451
src/core/astaroth.cu Normal file
View File

@@ -0,0 +1,451 @@
/*
Copyright (C) 2014-2018, Johannes Pekkilae, Miikka Vaeisalae.
This file is part of Astaroth.
Astaroth is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
Astaroth is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with Astaroth. If not, see <http://www.gnu.org/licenses/>.
*/
/**
* @file
* \brief Multi-GPU implementation.
*
* Detailed info.
*
*/
#include "astaroth.h"
#include "errchk.h"
#include "device.cuh"
#include "math_utils.h" // sum for reductions
#include "standalone/config_loader.h" // update_config
const char* intparam_names[] = {AC_FOR_INT_PARAM_TYPES(AC_GEN_STR)};
const char* realparam_names[] = {AC_FOR_REAL_PARAM_TYPES(AC_GEN_STR)};
const char* vtxbuf_names[] = {AC_FOR_VTXBUF_HANDLES(AC_GEN_STR)};
static const int MAX_NUM_DEVICES = 32;
static int num_devices = 1;
static Device devices[MAX_NUM_DEVICES] = {};
typedef struct {
int3 m;
int3 n;
} Grid;
static Grid
createGrid(const AcMeshInfo& config)
{
Grid grid;
grid.m = (int3) {
config.int_params[AC_mx],
config.int_params[AC_my],
config.int_params[AC_mz]
};
grid.n = (int3) {
config.int_params[AC_nx],
config.int_params[AC_ny],
config.int_params[AC_nz]
};
return grid;
}
static Grid grid; // A grid consists of num_devices subgrids
static Grid subgrid;
static int
gridIdx(const Grid& grid, const int i, const int j, const int k)
{
return i + j * grid.m.x + k * grid.m.x * grid.m.y;
}
static int3
gridIdx3d(const Grid& grid, const int idx)
{
return (int3){idx % grid.m.x,
(idx % (grid.m.x * grid.m.y)) / grid.m.x,
idx / (grid.m.x * grid.m.y)};
}
void
printInt3(const int3 vec)
{
printf("(%d, %d, %d)", vec.x, vec.y, vec.z);
}
AcResult
acInit(const AcMeshInfo& config)
{
// Check devices
cudaGetDeviceCount(&num_devices);
if (num_devices < 1) {
ERROR("No CUDA devices found!");
return AC_FAILURE;
}
if (num_devices > MAX_NUM_DEVICES) {
WARNING("More devices found than MAX_NUM_DEVICES. Using only MAX_NUM_DEVICES");
num_devices = MAX_NUM_DEVICES;
}
if (!AC_MULTIGPU_ENABLED) {
WARNING("MULTIGPU_ENABLED was false. Using only one device");
num_devices = 1; // Use only one device if multi-GPU is not enabled
}
// Check that num_devices is divisible with AC_nz. This makes decomposing the
// problem domain to multiple GPUs much easier since we do not have to worry
// about remainders
ERRCHK_ALWAYS(config.int_params[AC_nz] % num_devices == 0);
// Decompose the problem domain
// The main grid
grid = createGrid(config);
// Subgrids
AcMeshInfo subgrid_config = config;
subgrid_config.int_params[AC_nz] /= num_devices;
update_config(&subgrid_config);
subgrid = createGrid(subgrid_config);
// Periodic boundary conditions become weird if the system can "fold unto itself".
ERRCHK_ALWAYS(subgrid.n.x >= STENCIL_ORDER);
ERRCHK_ALWAYS(subgrid.n.y >= STENCIL_ORDER);
ERRCHK_ALWAYS(subgrid.n.z >= STENCIL_ORDER);
printf("Grid m "); printInt3(grid.m); printf("\n");
printf("Grid n "); printInt3(grid.n); printf("\n");
printf("Subrid m "); printInt3(subgrid.m); printf("\n");
printf("Subrid n "); printInt3(subgrid.n); printf("\n");
// Initialize the devices
for (int i = 0; i < num_devices; ++i) {
createDevice(i, subgrid_config, &devices[i]);
printDeviceInfo(devices[i]);
}
return AC_SUCCESS;
}
AcResult
acQuit(void)
{
for (int i = 0; i < num_devices; ++i) {
destroyDevice(devices[i]);
}
return AC_SUCCESS;
}
int
gridIdxx(const Grid grid, const int3 idx)
{
return gridIdx(grid, idx.x, idx.y, idx.z);
}
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
*/
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};
const int3 s0 = src;
const int3 s1 = gridIdx3d(grid, gridIdx(grid, s0.x, s0.y, s0.z) + num_vertices);
const int3 da = (int3){max(s0.x, d0.x), max(s0.y, d0.y), max(s0.z, d0.z)};
const int3 db = (int3){min(s1.x, d1.x), min(s1.y, d1.y), min(s1.z, d1.z)};
/*
printf("Device %d\n", i);
printf("\ts0: "); printInt3(s0); printf("\n");
printf("\td0: "); printInt3(d0); printf("\n");
printf("\tda: "); printInt3(da); printf("\n");
printf("\tdb: "); printInt3(db); printf("\n");
printf("\td1: "); printInt3(d1); printf("\n");
printf("\ts1: "); printInt3(s1); printf("\n");
printf("\t-> %s to device %d\n", db.z >= da.z ? "Copy" : "Do not copy", i);
*/
if (db.z >= da.z) {
const int copy_cells = gridIdxx(subgrid, db) - gridIdxx(subgrid, da);
const int3 da_local = (int3){da.x, da.y, da.z - i * grid.n.z / num_devices}; // DECOMPOSITION OFFSET HERE
// printf("\t\tcopy %d cells to local index ", copy_cells); printInt3(da_local); printf("\n");
copyMeshToDevice(devices[i], STREAM_PRIMARY, host_mesh, da, da_local, copy_cells);
}
printf("\n");
}
return AC_SUCCESS;
}
AcResult
acStoreWithOffset(const int3& src, const int num_vertices, AcMesh* host_mesh)
{
// See acLoadWithOffset() 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};
const int3 s0 = src;
const int3 s1 = gridIdx3d(grid, gridIdx(grid, s0.x, s0.y, s0.z) + num_vertices);
const int3 da = (int3){max(s0.x, d0.x), max(s0.y, d0.y), max(s0.z, d0.z)};
const int3 db = (int3){min(s1.x, d1.x), min(s1.y, d1.y), min(s1.z, d1.z)};
/*
printf("Device %d\n", i);
printf("\ts0: "); printInt3(s0); printf("\n");
printf("\td0: "); printInt3(d0); printf("\n");
printf("\tda: "); printInt3(da); printf("\n");
printf("\tdb: "); printInt3(db); printf("\n");
printf("\td1: "); printInt3(d1); printf("\n");
printf("\ts1: "); printInt3(s1); printf("\n");
printf("\t-> %s to device %d\n", db.z >= da.z ? "Copy" : "Do not copy", i);
*/
if (db.z >= da.z) {
const int copy_cells = gridIdxx(subgrid, db) - gridIdxx(subgrid, da);
const int3 da_local = (int3){da.x, da.y, da.z - i * grid.n.z / num_devices}; // DECOMPOSITION OFFSET HERE
// printf("\t\tcopy %d cells from local index ", copy_cells); printInt3(da_local); printf("\n");
copyMeshToHost(devices[i], STREAM_PRIMARY, da_local, da, copy_cells, host_mesh);
}
printf("\n");
}
return AC_SUCCESS;
}
// acCopyMeshToDevice
AcResult
acLoad(const AcMesh& host_mesh)
{
return acLoadWithOffset(host_mesh, (int3){0, 0, 0}, AC_VTXBUF_SIZE(host_mesh.info));
}
// acCopyMeshToHost
AcResult
acStore(AcMesh* host_mesh)
{
return acStoreWithOffset((int3){0, 0, 0}, AC_VTXBUF_SIZE(host_mesh->info), host_mesh);
}
AcResult
acIntegrateStep(const int& isubstep, const AcReal& dt)
{
const int3 start = (int3){STENCIL_ORDER/2, STENCIL_ORDER/2, STENCIL_ORDER/2};
const int3 end = (int3){STENCIL_ORDER/2 + subgrid.n.x,
STENCIL_ORDER/2 + subgrid.n.y,
STENCIL_ORDER/2 + subgrid.n.z};
for (int i = 0; i < num_devices; ++i) {
rkStep(devices[i], STREAM_PRIMARY, isubstep, start, end, dt);
}
return AC_SUCCESS;
}
AcResult
acBoundcondStep(void)
{
acSynchronize();
if (num_devices == 1) {
boundcondStep(devices[0], STREAM_PRIMARY,
(int3){0, 0, 0}, (int3){subgrid.m.x, subgrid.m.y, subgrid.m.z});
} else {
// Local boundary conditions
for (int i = 0; i < num_devices; ++i) {
const int3 d0 = (int3){0, 0, STENCIL_ORDER/2}; // DECOMPOSITION OFFSET HERE
const int3 d1 = (int3){subgrid.m.x, subgrid.m.y, d0.z + subgrid.n.z};
boundcondStep(devices[i], STREAM_PRIMARY, d0, d1);
}
/*
// ===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 manage
communication between GPUs.
printf("Boundconds best dims (%d, %d, %d) %f ms\n", best_dims.x, best_dims.y, best_dims.z, double(best_time) / NUM_ITERATIONS);
exit(0);
#else
const int depth = (int)ceil(mesh_info.int_params[AC_mz]/(float)num_devices);
const int3 start = (int3){0, 0, device_id * depth};
const int3 end = (int3){mesh_info.int_params[AC_mx],
mesh_info.int_params[AC_my],
min((device_id+1) * depth, mesh_info.int_params[AC_mz])};
const dim3 tpb(8,2,8);
// TODO uses the default stream currently
if (mesh_info.int_params[AC_bc_type] == 666) { // TODO MAKE A BETTER SWITCH
wedge_boundconds(0, tpb, start, end, d_buffer);
} else {
for (int i = 0; i < NUM_VTXBUF_HANDLES; ++i)
periodic_boundconds(0, tpb, start, end, d_buffer.in[i]);
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
*/
// Exchange halos
for (int i = 0; i < num_devices; ++i) {
const int num_vertices = subgrid.m.x * subgrid.m.y * STENCIL_ORDER/2;
// ...|ooooxxx|... -> xxx|ooooooo|...
{
const int3 src = (int3) {0, 0, subgrid.n.z};
const int3 dst = (int3) {0, 0, 0};
copyMeshDeviceToDevice(devices[i], STREAM_PRIMARY, src, devices[(i+1) % num_devices], dst, num_vertices);
}
// ...|ooooooo|xxx <- ...|xxxoooo|...
{
const int3 src = (int3) {0, 0, STENCIL_ORDER/2};
const int3 dst = (int3) {0, 0, STENCIL_ORDER/2 + subgrid.n.z};
copyMeshDeviceToDevice(devices[(i+1) % num_devices], STREAM_PRIMARY, src, devices[i], dst, num_vertices);
}
}
}
acSynchronize();
return AC_SUCCESS;
}
static AcResult
acSwapBuffers(void)
{
for (int i = 0; i < num_devices; ++i) {
swapBuffers(devices[i]);
}
return AC_SUCCESS;
}
AcResult
acIntegrate(const AcReal& dt)
{
for (int isubstep = 0; isubstep < 3; ++isubstep) {
acBoundcondStep();
acIntegrateStep(isubstep, dt);
acSwapBuffers();
}
return AC_SUCCESS;
}
AcReal
acReduceScal(const ReductionType& rtype,
const VertexBufferHandle& vtxbuffer_handle)
{
// TODO
return 0;
}
AcReal
acReduceVec(const ReductionType& rtype, const VertexBufferHandle& a,
const VertexBufferHandle& b, const VertexBufferHandle& c)
{
// TODO
return 0;
}
AcResult
acSynchronize(void)
{
for (int i = 0; i < num_devices; ++i) {
synchronize(devices[i], STREAM_ALL);
}
return AC_SUCCESS;
}