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pub
pi
Commits
174c2a1b
Commit
174c2a1b
authored
Mar 01, 2020
by
Neil Gershenfeld
Browse files
wip
parent
73d4808e
Pipeline
#5090
passed with stage
in 1 second
Changes
3
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1
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Inline
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CUDA/cudapi.cu
0 → 100755
View file @
174c2a1b
//
// cudapi.cu
// Neil Gershenfeld 3/1/20
// calculation of pi by a CUDA sum
// pi = 3.14159265358979323846
//
#include
<iostream>
#include
<chrono>
#include
<cstdint>
uint64_t
blocks
=
1024
;
uint64_t
threads
=
1024
;
uint64_t
nloop
=
1000000
;
uint64_t
npts
=
blocks
*
threads
;
__global__
void
init
(
double
*
arr
,
uint64_t
nloop
)
{
uint64_t
i
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
uint64_t
start
=
nloop
*
i
+
1
;
uint64_t
end
=
nloop
*
(
i
+
1
)
+
1
;
arr
[
i
]
=
0
;
for
(
uint64_t
j
=
start
;
j
<
end
;
++
j
)
arr
[
i
]
+=
0.5
/
((
j
-
0.75
)
*
(
j
-
0.25
));
}
__global__
void
reduce_sum
(
double
*
arr
,
uint64_t
len
)
{
uint64_t
i
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
if
(
i
<
len
)
arr
[
i
]
+=
arr
[
i
+
len
];
}
void
reduce
(
double
*
arr
)
{
uint64_t
len
=
npts
>>
1
;
while
(
1
)
{
reduce_sum
<<<
blocks
,
threads
>>>
(
arr
,
len
);
len
=
len
>>
1
;
if
(
len
==
0
)
return
;
}
}
int
main
(
void
)
{
double
harr
[
1
],
*
darr
;
cudaMalloc
(
&
darr
,
npts
*
sizeof
(
double
));
auto
tstart
=
std
::
chrono
::
high_resolution_clock
::
now
();
init
<<<
blocks
,
threads
>>>
(
darr
,
nloop
);
reduce
(
darr
);
cudaDeviceSynchronize
();
auto
tend
=
std
::
chrono
::
high_resolution_clock
::
now
();
auto
dt
=
std
::
chrono
::
duration_cast
<
std
::
chrono
::
microseconds
>
(
tend
-
tstart
).
count
();
auto
mflops
=
npts
*
nloop
*
5.0
/
dt
;
cudaMemcpy
(
harr
,
darr
,
8
,
cudaMemcpyDeviceToHost
);
printf
(
"npts = %ld, nloop = %ld, pi = %lf
\n
"
,
npts
,
nloop
,
harr
[
0
]);
printf
(
"time = %f, estimated MFlops = %f
\n
"
,
1e-6
*
dt
,
mflops
);
cudaFree
(
darr
);
return
0
;
}
Python/numbapig.py
View file @
174c2a1b
#
# numbapig.py
# Neil Gershenfeld
2/9
/20
# calculation of pi by a Numba
GPU
sum
# Neil Gershenfeld
3/1
/20
# calculation of pi by a Numba
CUDA
sum
# pi = 3.14159265358979323846
#
from
numba
import
cuda
import
numpy
as
np
import
time
#
# problem size
#
block_size
=
2
**
10
grid_size
=
2
**
21
NPTS
=
grid_size
*
block_size
#
# kernels and functions
#
block_size
=
1024
grid_size
=
1024
nloop
=
1000000
npts
=
grid_size
*
block_size
@
cuda
.
jit
def
init
(
arr
):
i
=
1
+
cuda
.
grid
(
1
)
arr
[
i
-
1
]
=
0.5
/
((
i
-
0.75
)
*
(
i
-
0.25
))
#arr[i-1] = i # for testing reduction
#
@
cuda
.
reduce
def
Numba_reduce
(
a
,
b
):
return
a
+
b
#
def
init
(
arr
,
nloop
):
i
=
cuda
.
blockIdx
.
x
*
cuda
.
blockDim
.
x
+
cuda
.
threadIdx
.
x
;
start
=
nloop
*
i
+
1
;
end
=
nloop
*
(
i
+
1
)
+
1
;
arr
[
i
]
=
0
;
for
j
in
range
(
start
,
end
):
arr
[
i
]
+=
0.5
/
((
j
-
0.75
)
*
(
j
-
0.25
));
@
cuda
.
jit
def
CUDA_sum
(
arr
,
len
):
i
=
cuda
.
grid
(
1
)
if
(
i
<
len
):
arr
[
i
]
+=
arr
[
i
+
len
]
#
def
CUDA_reduce
(
arr
,
NPTS
):
len
=
NPTS
>>
1
def
reduce_sum
(
arr
,
len
):
i
=
cuda
.
blockIdx
.
x
*
cuda
.
blockDim
.
x
+
cuda
.
threadIdx
.
x
;
if
(
i
<
len
):
arr
[
i
]
+=
arr
[
i
+
len
]
def
reduce
(
arr
,
npts
):
len
=
npts
>>
1
while
(
1
):
CUDA_sum
[
grid_size
,
block_size
](
arr
,
len
)
cuda
.
synchronize
()
reduce_sum
[
grid_size
,
block_size
](
arr
,
len
)
len
=
len
>>
1
if
(
len
==
0
):
return
#
@
cuda
.
jit
def
CUDA_result
(
arr
,
result
):
i
=
cuda
.
grid
(
1
)
if
(
i
==
0
):
result
[
0
]
=
arr
[
0
]
#
# device array
#
arr
=
cuda
.
device_array
(
NPTS
,
np
.
float32
)
result
=
cuda
.
device_array
(
1
,
np
.
float32
)
#arr = cuda.device_array(NPTS,np.int64) # for testing reduction
#result = cuda.device_array(1,np.int64) # for testing reduction
#
# compile kernels by calling them
#
init
[
grid_size
,
block_size
](
arr
)
pi
=
Numba_reduce
(
arr
)
CUDA_reduce
(
arr
,
NPTS
)
CUDA_result
(
arr
,
result
)
#
# CUDA kernel array calculation
#
start_time
=
time
.
time
()
init
[
grid_size
,
block_size
](
arr
)
cuda
.
synchronize
()
end_time
=
time
.
time
()
mflops
=
NPTS
*
4.0
/
(
1.0e6
*
(
end_time
-
start_time
))
print
(
"CUDA kernel array calculation:"
)
print
(
" time = %f, estimated MFlops = %f"
%
(
end_time
-
start_time
,
mflops
))
#
# Numba reduce
#
init
[
grid_size
,
block_size
](
arr
)
start_time
=
time
.
time
()
pi
=
Numba_reduce
(
arr
)
end_time
=
time
.
time
()
mflops
=
NPTS
*
1.0
/
(
1.0e6
*
(
end_time
-
start_time
))
print
(
"Numba reduce:"
)
print
(
" time = %f, estimated MFlops = %f"
%
(
end_time
-
start_time
,
mflops
))
#
# both with Numba reduce
#
darr
=
cuda
.
device_array
(
npts
,
np
.
float64
)
init
[
grid_size
,
block_size
](
darr
,
nloop
)
# compile kernel
reduce
(
darr
,
npts
)
# compile kernel
start_time
=
time
.
time
()
init
[
grid_size
,
block_size
](
arr
)
init
[
grid_size
,
block_size
](
darr
,
nloop
)
reduce
(
darr
,
npts
)
cuda
.
synchronize
()
pi
=
Numba_reduce
(
arr
)
end_time
=
time
.
time
()
mflops
=
NPTS
*
5.0
/
(
1.0e6
*
(
end_time
-
start_time
))
print
(
"both with Numba reduce:"
)
print
(
" NPTS = %d, pi = %f"
%
(
NPTS
,
pi
))
print
(
" time = %f, estimated MFlops = %f"
%
(
end_time
-
start_time
,
mflops
))
#
# CUDA kernel reduction
#
init
[
grid_size
,
block_size
](
arr
)
start_time
=
time
.
time
()
CUDA_reduce
(
arr
,
NPTS
)
end_time
=
time
.
time
()
mflops
=
NPTS
*
1.0
/
(
1.0e6
*
(
end_time
-
start_time
))
print
(
"CUDA kernel reduction:"
)
print
(
" time = %f, estimated MFlops = %f"
%
(
end_time
-
start_time
,
mflops
))
#
# both with CUDA kernel reduction
#
start_time
=
time
.
time
()
init
[
grid_size
,
block_size
](
arr
)
cuda
.
synchronize
()
CUDA_reduce
(
arr
,
NPTS
)
CUDA_result
(
arr
,
result
)
cuda
.
synchronize
()
end_time
=
time
.
time
()
pi
=
result
.
copy_to_host
()
mflops
=
NPTS
*
5.0
/
(
1.0e6
*
(
end_time
-
start_time
))
print
(
"both with CUDA kernel reduction:"
)
print
(
" NPTS = %d, pi = %f"
%
(
NPTS
,
pi
[
0
]))
print
(
" time = %f, estimated MFlops = %f"
%
(
end_time
-
start_time
,
mflops
))
#
# both with CUDA kernel reduction and transfer
#
start_time
=
time
.
time
()
init
[
grid_size
,
block_size
](
arr
)
CUDA_reduce
(
arr
,
NPTS
)
CUDA_result
(
arr
,
result
)
pi
=
result
.
copy_to_host
()
end_time
=
time
.
time
()
mflops
=
NPTS
*
5.0
/
(
1.0e6
*
(
end_time
-
start_time
))
print
(
"both with CUDA kernel reduction and transfer:"
)
print
(
"
NPTS
= %d, pi = %f"
%
(
NPTS
,
pi
[
0
]))
print
(
"
time = %f, estimated MFlops = %f"
%
(
end_time
-
start_time
,
mflops
))
mflops
=
npts
*
nloop
*
5.0
/
(
1.0e6
*
(
end_time
-
start_time
))
harr
=
darr
.
copy_to_host
(
)
print
(
"
npts = %d, nloop
= %d, pi = %f"
%
(
npts
,
nloop
,
harr
[
0
]))
print
(
"time = %f, estimated MFlops = %f"
%
(
end_time
-
start_time
,
mflops
))
README.md
View file @
174c2a1b
...
...
@@ -6,6 +6,8 @@
|88,333|
[
mpimppi.c
](
hybrid/mpimppi.c
)
|C, MPI+OpenMP, 1024 nodes, 64 cores/node, 4 threads/core
<br>
cc mpimppi.c -o mpimppi -O3 -ffast-math -fopenmp|Argonne ALCF Theta
<br>
Cray XC40|Oct 9, 2019|
|2,117|
[
mpipi2.c
](
MPI/mpipi2.c
)
|C, MPI, 10 nodes, 96 cores/node
<br>
mpicc mpipi2.c -o mpipi2 -O3 -ffast-math|Intel 2x Xeon Platinum 8175M|Oct 24, 2019|
|2,102|
[
mpipi2.py
](
Python/mpipi2.py
)
|Python, Numba, MPI
<br>
10 nodes, 96 cores/node|Intel 2x Xeon Platinum 8175M|Feb 6, 2020|
|1,635|
[
cudapi.cu
](
CUDA/cudapi.cu
)
|C++, CUDA
<br>
5120 cores|NVIDIA V100|March 1, 2020|
|1,090|
[
numbapig.py
](
Python/numbapig.py
)
|Python, Numba, CUDA
<br>
5120 cores|NVIDIA V100|March 1, 2020|
|315|
[
numbapip.py
](
Python/numbapip.py
)
|Python, Numba, parallel, fastmath
<br>
96 cores|Intel 2x Xeon Platinum 8175M|Feb 7, 2020|
|272|
[
threadpi.c
](
C/threadpi.c
)
|C, 96 threads
<br>
gcc threadpi.c -o threadpi -O3 -ffast-math -pthread|Intel 2x Xeon Platinum 8175M|Jun 3, 2019|
|211|
[
mpipi2.c
](
MPI/mpipi2.c
)
|C, MPI, 1 node, 96 cores
<br>
mpicc mpipi2.c -o mpipi2 -O3 -ffast-math|Intel 2x Xeon Platinum 8175M|Oct 24, 2019|
...
...
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