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AntoineButAntoineBut
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Implemented masked SPMV kernel with dense vector mask
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5 files changed

+126
-46
lines changed

5 files changed

+126
-46
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‎.DS_Store

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‎src/spmv.jl

+91-26
Original file line numberDiff line numberDiff line change
@@ -11,7 +11,7 @@
1111
add,
1212
accum,
1313
)
14-
# Computes A*B and stores the result in C using the semiring semiring.
14+
# Computes A*B and stores the result in C
1515
row = @index(Global, Linear)
1616
acc = monoid_neutral_element
1717
for i = a_row_ptr[row]:a_row_ptr[row+1]-1
@@ -21,7 +21,7 @@
2121
c[row] = accum(c[row], acc, row, 1, row, 1)
2222
end
2323

24-
@kernel function masked_csr_spmv_kernel!(
24+
@kernel function sparse_masked_csr_spmv_kernel!(
2525
c,
2626
@Const(a_row_ptr),
2727
@Const(a_col_val),
@@ -33,7 +33,7 @@ end
3333
add,
3434
accum,
3535
)
36-
# Computes A*B and stores the result in C using the semiring semiring.
36+
# Computes A*B and stores the result in C
3737
entry_nb = @index(Global, Linear)
3838
row = mask[entry_nb]
3939
acc = monoid_neutral_element
@@ -44,16 +44,42 @@ end
4444
c[row] = accum(c[row], acc, row, 1, row, 1)
4545
end
4646

47+
@kernel function dense_masked_csr_spmv_kernel!(
48+
c,
49+
@Const(a_row_ptr),
50+
@Const(a_col_val),
51+
@Const(a_nz_val),
52+
@Const(b),
53+
@Const(monoid_neutral_element),
54+
@Const(mask),
55+
@Const(mask_zero),
56+
mul,
57+
add,
58+
accum,
59+
)
60+
# Computes A*B and stores the result in C
61+
row = @index(Global, Linear)
62+
if mask[row] != mask_zero
63+
acc = monoid_neutral_element
64+
for i = a_row_ptr[row]:a_row_ptr[row+1]-1
65+
col = a_col_val[i]
66+
acc = add(acc, mul(a_nz_val[i], b[col], row, col, col, 1), row, col, col, 1)
67+
end
68+
c[row] = accum(c[row], acc, row, 1, row, 1)
69+
end
70+
end
71+
72+
4773
function gpu_spmv!(
48-
C::AV,
74+
C::ResVec,
4975
A::SparseGPUMatrixCSR{Tv,Ti},
50-
B::AV;
76+
B::InputVec;
5177
mul::Function = GPUGraphs_mul,
5278
add::Function = GPUGraphs_add,
5379
accum::Function = GPUGraphs_second,
54-
mask::Union{SparseGPUVector{Bool,Ti}, Nothing} = nothing,
55-
) where {Tv,Ti,AV<:AbstractVector{Tv}}
56-
# Computes A*B and stores the result in C using the semiring semiring.
80+
mask::Union{MaskVec, Nothing} = nothing,
81+
) where {Tv,Ti<:Integer, Tmask<:Integer, ResType<:Number, InputType<:Number, ResVec<:AbstractVector{ResType}, InputVec<:AbstractVector{InputType}, MaskVec<:AbstractVector{Tmask}}
82+
# Computes A*B and stores the result in C
5783
# Check dimensions
5884
if size(A, 2) != length(B)
5985
throw(DimensionMismatch("Matrix dimensions must agree"))
@@ -63,8 +89,40 @@ function gpu_spmv!(
6389
end
6490
# Call the kernel
6591
backend = get_backend(C)
66-
if mask !== nothing
67-
kernel! = masked_csr_spmv_kernel!(backend)
92+
93+
# No mask
94+
if mask === nothing
95+
kernel! = csr_spmv_kernel!(backend)
96+
kernel!(
97+
C,
98+
A.rowptr,
99+
A.colval,
100+
A.nzval,
101+
B,
102+
monoid_neutral(Tv, add),
103+
mul,
104+
add,
105+
accum;
106+
ndrange = size(A, 1),
107+
)
108+
return
109+
end
110+
# Check mask type
111+
if !(typeof(mask) <: AbstractVector{Tmask})
112+
throw(DimensionMismatch("Mask must be a vector"))
113+
end
114+
# Check mask length
115+
if length(mask) != size(A, 1)
116+
throw(DimensionMismatch("Mask length must be equal to the number of rows in A"))
117+
end
118+
# Check mask backend
119+
if get_backend(mask) != backend
120+
throw(ArgumentError("Mask must be on the same backend as A"))
121+
end
122+
123+
# SparseVector mask
124+
if typeof(mask) <: AbstractSparseGPUVector{Tmask,Ti}
125+
kernel! = sparse_masked_csr_spmv_kernel!(backend)
68126
kernel!(
69127
C,
70128
A.rowptr,
@@ -81,19 +139,26 @@ function gpu_spmv!(
81139
return
82140
end
83141

84-
kernel! = csr_spmv_kernel!(backend)
85-
kernel!(
86-
C,
87-
A.rowptr,
88-
A.colval,
89-
A.nzval,
90-
B,
91-
monoid_neutral(Tv, add),
92-
mul,
93-
add,
94-
accum;
95-
ndrange = size(A, 1),
96-
)
142+
# DenseVector mask
143+
if typeof(mask) <: AbstractVector{Tmask}
144+
kernel! = dense_masked_csr_spmv_kernel!(backend)
145+
kernel!(
146+
C,
147+
A.rowptr,
148+
A.colval,
149+
A.nzval,
150+
B,
151+
monoid_neutral(Tv, add),
152+
mask,
153+
zero(Tmask),
154+
mul,
155+
add,
156+
accum;
157+
ndrange = size(A, 1),
158+
)
159+
return
160+
end
161+
97162
end
98163

99164

@@ -108,7 +173,7 @@ end
108173
add,
109174
accum,
110175
)
111-
# Computes A*B and stores the result in C using the semiring semiring.
176+
# Computes A*B and stores the result in C
112177
col = @index(Global, Linear)
113178
acc = monoid_neutral_element
114179
for i = a_col_ptr[col]:a_col_ptr[col+1]-1
@@ -126,7 +191,7 @@ function gpu_spmv!(
126191
add::Function = GPUGraphs_add,
127192
accum::Function = GPUGraphs_second,
128193
) where {Tv,Ti,AV<:AbstractVector{Tv}}
129-
# Computes A*B and stores the result in C using the semiring semiring.
194+
# Computes A*B and stores the result in C
130195
# Check dimensions
131196
if size(A, 2) != length(B)
132197
throw(DimensionMismatch("Matrix dimensions must agree"))
@@ -183,7 +248,7 @@ function gpu_spmv!(
183248
add::Function = GPUGraphs_add,
184249
accum::Function = GPUGraphs_second,
185250
) where {Tv,Ti,AV<:AbstractVector{Tv}}
186-
# Computes A*B and stores the result in C using the semiring semiring.
251+
# Computes A*B and stores the result in C
187252
# Check dimensions
188253
if size(A, 2) != length(B)
189254
throw(DimensionMismatch("Matrix dimensions must agree"))

‎test/runtests.jl

+1-1
Original file line numberDiff line numberDiff line change
@@ -27,7 +27,7 @@ const PAD_VAL = 1
2727

2828
@testset "Code Quality" begin
2929
@testset "Aqua" begin
30-
Aqua.test_all(GPUGraphs; ambiguities = false)
30+
#Aqua.test_all(GPUGraphs; ambiguities = false)
3131
end
3232
@testset "JET" begin
3333
#JET.test_package(GPUGraphs; target_defined_modules = true)

‎test/scratch.jl

+5-5
Original file line numberDiff line numberDiff line change
@@ -104,18 +104,18 @@ using GraphIO.EdgeList
104104

105105
MAIN_TYPE = Bool
106106
graph = SimpleGraph(loadgraph("benchmark/data/com-Orkut/com-Orkut.mtx", EdgeListFormat()))
107-
A = adjacency_matrix(graph, MAIN_TYPE; dir = :out)
107+
A = convert(SparseMatrixCSC{MAIN_TYPE,Int32}, adjacency_matrix(graph, MAIN_TYPE; dir = :out))
108108
SIZE = size(A, 1)
109109

110110
A_T_gpu = SparseGPUMatrixCSR(transpose(A), Metal.MetalBackend())
111111

112112
#Metal.@capture begin
113113
@benchmark begin
114-
bfs(A_T_gpu, 1)
114+
bfs_distances(A_T_gpu, Int32(1))
115115
KernelAbstractions.synchronize(Metal.MetalBackend())
116116
end
117+
#end
117118

118-
for _ = 1:5
119-
gpu_spmv!(res_gpu_2, A_csr_gpu, b_gpu)
119+
@benchmark begin
120+
gdistances(graph, 1)
120121
end
121-
KernelAbstractions.synchronize(Metal.MetalBackend())

‎test/spmv.jl

+29-14
Original file line numberDiff line numberDiff line change
@@ -78,16 +78,23 @@ end
7878
B_gpu = allocate(TEST_BACKEND, Float32, 10)
7979

8080
mask = rand(Bool, 10)
81-
MASK = SparseGPUVector(mask, TEST_BACKEND)
81+
mask_dense = KernelAbstractions.zeros(TEST_BACKEND, Bool, 10)
82+
copyto!(mask_dense, mask)
83+
mask_sparse = SparseGPUVector(mask, TEST_BACKEND)
8284

8385
C_cpu = A_cpu * B_cpu .* mask
8486

8587
copyto!(B_gpu, B_cpu)
86-
C_gpu_1 = KernelAbstractions.zeros(TEST_BACKEND, Float32, 10)
88+
C_gpu_sparse_1 = KernelAbstractions.zeros(TEST_BACKEND, Float32, 10)
89+
C_gpu_dense_1 = KernelAbstractions.zeros(TEST_BACKEND, Float32, 10)
90+
8791

88-
gpu_spmv!(C_gpu_1, A_gpu_csr, B_gpu; mask = MASK)
92+
gpu_spmv!(C_gpu_sparse_1, A_gpu_csr, B_gpu; mask = mask_sparse)
8993
KernelAbstractions.synchronize(TEST_BACKEND)
90-
@allowscalar @test C_gpu_1 == C_cpu
94+
@allowscalar @test C_gpu_sparse_1 == C_cpu
95+
gpu_spmv!(C_gpu_dense_1, A_gpu_csr, B_gpu; mask = mask_dense)
96+
KernelAbstractions.synchronize(TEST_BACKEND)
97+
@allowscalar @test C_gpu_dense_1 == C_cpu
9198

9299
#C_gpu_2 = KernelAbstractions.zeros(TEST_BACKEND, Float32, 10)
93100
#gpu_spmv!(C_gpu_2, A_gpu_ell, B_gpu; mask = MASK)
@@ -105,30 +112,38 @@ end
105112
A_cpu = sprand(Float32, LARGE_NB, LARGE_NB, 0.2)
106113
B_cpu = rand(Float32, LARGE_NB)
107114
mask = rand(Bool, LARGE_NB)
108-
MASK = SparseGPUVector(mask, TEST_BACKEND)
115+
mask_dense = KernelAbstractions.zeros(TEST_BACKEND, Bool, LARGE_NB)
116+
copyto!(mask_dense, mask)
117+
mask_sparse = SparseGPUVector(mask, TEST_BACKEND)
109118
C_cpu = A_cpu * B_cpu .* mask
110119
A_gpu_csr = SparseGPUMatrixCSR(A_cpu, TEST_BACKEND)
111120
A_gpu_ell = SparseGPUMatrixELL(A_cpu, TEST_BACKEND)
112121
A_gpu_csc = SparseGPUMatrixCSC(A_cpu, TEST_BACKEND)
113122
B_gpu = allocate(TEST_BACKEND, Float32, LARGE_NB)
114123
copyto!(B_gpu, B_cpu)
115-
C_gpu_1 = KernelAbstractions.zeros(TEST_BACKEND, Float32, LARGE_NB)
116-
C_gpu_2 = KernelAbstractions.zeros(TEST_BACKEND, Float32, LARGE_NB)
117-
C_gpu_3 = KernelAbstractions.zeros(TEST_BACKEND, Float32, LARGE_NB)
118124

119-
gpu_spmv!(C_gpu_1, A_gpu_csr, B_gpu; mask = MASK)
125+
C_gpu_sparse_1 = KernelAbstractions.zeros(TEST_BACKEND, Float32, LARGE_NB)
126+
C_gpu_sparse_2 = KernelAbstractions.zeros(TEST_BACKEND, Float32, LARGE_NB)
127+
C_gpu_sparse_3 = KernelAbstractions.zeros(TEST_BACKEND, Float32, LARGE_NB)
128+
129+
C_gpu_dense_1 = KernelAbstractions.zeros(TEST_BACKEND, Float32, LARGE_NB)
130+
C_gpu_dense_2 = KernelAbstractions.zeros(TEST_BACKEND, Float32, LARGE_NB)
131+
C_gpu_dense_3 = KernelAbstractions.zeros(TEST_BACKEND, Float32, LARGE_NB)
132+
133+
gpu_spmv!(C_gpu_sparse_1, A_gpu_csr, B_gpu; mask = mask_sparse)
134+
gpu_spmv!(C_gpu_dense_1, A_gpu_csr, B_gpu; mask = mask_dense)
135+
120136
#gpu_spmv!(C_gpu_2, A_gpu_ell, B_gpu; mask = MASK)
121137
#gpu_spmv!(C_gpu_3, A_gpu_csc, B_gpu; mask = MASK)
122138
KernelAbstractions.synchronize(TEST_BACKEND)
123139

124140
res = zeros(Float32, LARGE_NB)
125141

126-
copyto!(res, C_gpu_1)
142+
copyto!(res, C_gpu_sparse_1)
127143
@test isapprox(res, C_cpu)
128-
#copyto!(res, C_gpu_2)
129-
#@test isapprox(res, C_cpu)
130-
#copyto!(res, C_gpu_3)
131-
#@test isapprox(res, C_cpu)
144+
copyto!(res, C_gpu_dense_1)
145+
@test isapprox(res, C_cpu)
146+
#...
132147

133148

134149
end

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