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//------------------------------------------------------------------------------
// GraphBLAS/CUDA/template/GB_cuda_jit_AxB_dot3_phase3_mp.cuh
//------------------------------------------------------------------------------
// SuiteSparse:GraphBLAS, Timothy A. Davis, (c) 2017-2025, All Rights Reserved.
// This file: Copyright (c) 2024-2025, NVIDIA CORPORATION. All rights reserved.
// SPDX-License-Identifier: Apache-2.0
//------------------------------------------------------------------------------
// This CUDA kernel produces the semi-ring product of two sparse matrices of
// types GB_A_TYPE and GB_B_TYPE and common index space size n, to a output
// matrix of type GB_C_TYPE. The matrices are sparse, with different numbers of
// non-zeros and different sparsity patterns. ie. we want to produce C = A'*B
// in the sense of the given semi-ring.
// This version uses a merge-path algorithm, when the sizes nnzA and nnzB are
// relatively close in size, neither is very sparse nor dense, for any size of
// N. Handles arbitrary sparsity patterns with guaranteed load balance.
// Both the grid and block are 1D, so blockDim.x is the # threads in a
// threadblock, and the # of threadblocks is grid.x
// Let b = blockIdx.x, and let s be blockDim.x. s= 32 with a variable number of
// active threads = min( min(g_xnz, g_ynz), 32)
// Thus, threadblock b owns a part of the index set spanned by g_xi and g_yi.
// Its job is to find the intersection of the index sets g_xi and g_yi, perform
// the semi-ring dot product on those items in the intersection, and finally
// reduce this data to a scalar, on exit write it to g_odata [b].
// int64_t start <- start of vector pairs for this kernel
// int64_t end <- end of vector pairs for this kernel
// int64_t *Bucket <- array of pair indices for all kernels
// GrB_Matrix C <- result matrix
// GrB_Matrix M <- mask matrix
// GrB_Matrix A <- input matrix A
// GrB_Matrix B <- input matrix B
//------------------------------------------------------------------------------
// GB_cuda_AxB_dot3_phase3_mp_kernel
//------------------------------------------------------------------------------
//#include <time.h>
__global__ void GB_cuda_AxB_dot3_phase3_mp_kernel
(
int64_t start,
int64_t end,
int64_t *Bucket, // do the work in Bucket [start:end-1]
GrB_Matrix C,
GrB_Matrix M,
GrB_Matrix A,
GrB_Matrix B,
const void *theta
)
{
#if !GB_A_IS_PATTERN
const GB_A_TYPE *__restrict__ Ax = (GB_A_TYPE *)A->x ;
#endif
#if !GB_B_IS_PATTERN
const GB_B_TYPE *__restrict__ Bx = (GB_B_TYPE *)B->x ;
#endif
GB_C_TYPE *__restrict__ Cx = (GB_C_TYPE *)C->x ;
GB_Ci_SIGNED_TYPE *__restrict__ Ci = (GB_Ci_SIGNED_TYPE *) C->i ;
const GB_Mp_TYPE *__restrict__ Mp = (GB_Mp_TYPE *) M->p ;
const GB_Mi_TYPE *__restrict__ Mi = (GB_Mi_TYPE *) M->i ;
#if GB_M_IS_HYPER
const GB_Mj_TYPE *__restrict__ Mh = (GB_Mj_TYPE *) M->h ;
#endif
// A and B are either sparse or hypersparse
const GB_Ai_TYPE *__restrict__ Ai = (GB_Ai_TYPE *) A->i ;
const GB_Bi_TYPE *__restrict__ Bi = (GB_Bi_TYPE *) B->i ;
const GB_Ap_TYPE *__restrict__ Ap = (GB_Ap_TYPE *) A->p ;
const GB_Bp_TYPE *__restrict__ Bp = (GB_Bp_TYPE *) B->p ;
ASSERT (GB_A_IS_HYPER || GB_A_IS_SPARSE) ;
ASSERT (GB_B_IS_HYPER || GB_B_IS_SPARSE) ;
#if GB_A_IS_HYPER
const int64_t anvec = A->nvec ;
const GB_Aj_TYPE *__restrict__ Ah = (GB_Aj_TYPE *) A->h ;
const void *A_Yp = (void *) ((A->Y == NULL) ? NULL : A->Y->p) ;
const void *A_Yi = (void *) ((A->Y == NULL) ? NULL : A->Y->i) ;
const void *A_Yx = (void *) ((A->Y == NULL) ? NULL : A->Y->x) ;
const int64_t A_hash_bits = (A->Y == NULL) ? 0 : (A->Y->vdim - 1) ;
#endif
#if GB_B_IS_HYPER
const int64_t bnvec = B->nvec ;
const GB_Bj_TYPE *__restrict__ Bh = (GB_Bj_TYPE *) B->h ;
const void *B_Yp = (void *) ((B->Y == NULL) ? NULL : B->Y->p) ;
const void *B_Yi = (void *) ((B->Y == NULL) ? NULL : B->Y->i) ;
const void *B_Yx = (void *) ((B->Y == NULL) ? NULL : B->Y->x) ;
const int64_t B_hash_bits = (B->Y == NULL) ? 0 : (B->Y->vdim - 1) ;
#endif
// zombie count
uint64_t zc = 0;
// set thread ID
// int tid_global = threadIdx.x+ blockDim.x* blockIdx.x;
int tid = threadIdx.x;
thread_block_tile<tile_sz> tile = tiled_partition<tile_sz>( this_thread_block());
int all_in_one = ( (end - start) == Mp [(M->nvec)] ) ;
// Main loop over pairs
int64_t kk ;
for (kk = start+ blockIdx.x; // warp per C(i,j)=A(:,i)'*B(:,j) dot product
kk < end;
kk += gridDim.x )
{
//----------------------------------------------------------------------
// get A(:,i) and B(:,j)
//----------------------------------------------------------------------
int64_t pair_id = all_in_one ? kk : Bucket [kk] ;
int64_t i = Mi[pair_id];
int64_t k = Ci[pair_id] >> 4;
// assert: Ci [pair_id] & 0xF == GB_BUCKET_MERGEPATH
// j = k or j = Mh [k] if C and M are hypersparse
int64_t j = GBh_M (Mh, k) ;
// find A(:,i)
int64_t pA_start, pA_end ;
#if GB_A_IS_HYPER
GB_hyper_hash_lookup (GB_Ap_IS_32, GB_Aj_IS_32,
Ah, anvec, Ap, A_Yp, A_Yi, A_Yx, A_hash_bits,
i, &pA_start, &pA_end) ;
#else
pA_start = Ap[i] ;
pA_end = Ap[i+1] ;
#endif
// find B(:,j)
int64_t pB_start, pB_end ;
#if GB_B_IS_HYPER
GB_hyper_hash_lookup (GB_Bp_IS_32, GB_Bj_IS_32,
Bh, bnvec, Bp, B_Yp, B_Yi, B_Yx, B_hash_bits,
j, &pB_start, &pB_end) ;
#else
pB_start = Bp[j] ;
pB_end = Bp[j+1] ;
#endif
//----------------------------------------------------------------------
// compute cij
//----------------------------------------------------------------------
__shared__ int64_t Xi_s[shared_vector_size];
__shared__ int64_t Yi_s[shared_vector_size];
GB_DECLAREA (aki) ;
GB_DECLAREB (bkj) ;
GB_DECLARE_IDENTITY (cij) ; // GB_Z_TYPE cij = identity
int cij_exists = 0 ;
// int64_t total_ainz = pA_start - pA_end ;
// int64_t total_bjnz = pB_start - pB_end ;
// if (total_ainz < total_bjnz)
{
// A(:,i) is sparser than B(:,j)
#define MP_FLIP 0
#define pX pA
#define pX_start pA_start
#define pX_end pA_end
#define Xi Ai
#define pY pB
#define pY_start pB_start
#define pY_end pB_end
#define Yi Bi
#include "GB_cuda_jit_AxB_dot3_phase3_mp_guts.cuh"
}
#if 0
else
{
// B(:,j) is sparser than A(:,i)
// (this works but it has the same performance)
#define MP_FLIP 1
#define pX pB
#define pX_start pB_start
#define pX_end pB_end
#define Xi Bi
#define pY pA
#define pY_start pA_start
#define pY_end pA_end
#define Yi Ai
// flip the roles of A(:,i) and B(:,j)
#include "GB_cuda_jit_AxB_dot3_phase3_mp_guts.cuh"
}
#endif
//----------------------------------------------------------------------
// reduce sum per-thread values to a single scalar, get OR of flag
//----------------------------------------------------------------------
// Do vote here for control.
cij_exists = tile.any (cij_exists) ;
tile.sync ( ) ;
#if !GB_C_ISO
if (cij_exists)
{
// FIXME: the ANY monoid needs the cij_exists for each thread
cij = GB_cuda_tile_reduce_ztype (tile, cij) ;
}
#endif
// HACK
//int64_t end_time = (int64_t) clock ( ) ;
//cij = end_time - start_time ;
//cij_exists = 1 ;
// write result for this block to global mem
if (tid == 0)
{
if (cij_exists)
{
// Cx [pair_id] = (GB_C_TYPE) cij
GB_PUTC (cij, Cx, pair_id) ;
Ci [pair_id] = i ;
}
else
{
// cij is a zombie
zc++;
Ci [pair_id] = GB_ZOMBIE (i) ;
}
}
//__syncthreads();
}
//--------------------------------------------------------------------------
// sum up the global zombie count
//--------------------------------------------------------------------------
if( tid ==0 && zc > 0)
{
GB_cuda_atomic_add <uint64_t>( &(C->nzombies), zc) ;
}
}
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