cuda-samples/Samples/eigenvalues/bisect_kernel_large_multi.cuh
2021-10-21 16:34:49 +05:30

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/* Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
*
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* * Neither the name of NVIDIA CORPORATION nor the names of its
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*
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/* Perform second step of bisection algorithm for large matrices for
* intervals that contained after the first step more than one eigenvalue
*/
#ifndef _BISECT_KERNEL_LARGE_MULTI_H_
#define _BISECT_KERNEL_LARGE_MULTI_H_
#include <cooperative_groups.h>
namespace cg = cooperative_groups;
// includes, project
#include "config.h"
#include "util.h"
// additional kernel
#include "bisect_util.cu"
////////////////////////////////////////////////////////////////////////////////
//! Perform second step of bisection algorithm for large matrices for
//! intervals that after the first step contained more than one eigenvalue
//! @param g_d diagonal elements of symmetric, tridiagonal matrix
//! @param g_s superdiagonal elements of symmetric, tridiagonal matrix
//! @param n matrix size
//! @param blocks_mult start addresses of blocks of intervals that are
//! processed by one block of threads, each of the
//! intervals contains more than one eigenvalue
//! @param blocks_mult_sum total number of eigenvalues / singleton intervals
//! in one block of intervals
//! @param g_left left limits of intervals
//! @param g_right right limits of intervals
//! @param g_left_count number of eigenvalues less than left limits
//! @param g_right_count number of eigenvalues less than right limits
//! @param g_lambda final eigenvalue
//! @param g_pos index of eigenvalue (in ascending order)
//! @param precision desired precision of eigenvalues
////////////////////////////////////////////////////////////////////////////////
__global__ void bisectKernelLarge_MultIntervals(
float *g_d, float *g_s, const unsigned int n, unsigned int *blocks_mult,
unsigned int *blocks_mult_sum, float *g_left, float *g_right,
unsigned int *g_left_count, unsigned int *g_right_count, float *g_lambda,
unsigned int *g_pos, float precision) {
// Handle to thread block group
cg::thread_block cta = cg::this_thread_block();
const unsigned int tid = threadIdx.x;
// left and right limits of interval
__shared__ float s_left[2 * MAX_THREADS_BLOCK];
__shared__ float s_right[2 * MAX_THREADS_BLOCK];
// number of eigenvalues smaller than interval limits
__shared__ unsigned int s_left_count[2 * MAX_THREADS_BLOCK];
__shared__ unsigned int s_right_count[2 * MAX_THREADS_BLOCK];
// helper array for chunk compaction of second chunk
__shared__ unsigned int s_compaction_list[2 * MAX_THREADS_BLOCK + 1];
// compaction list helper for exclusive scan
unsigned int *s_compaction_list_exc = s_compaction_list + 1;
// flag if all threads are converged
__shared__ unsigned int all_threads_converged;
// number of active threads
__shared__ unsigned int num_threads_active;
// number of threads to employ for compaction
__shared__ unsigned int num_threads_compaction;
// flag if second chunk has to be compacted
__shared__ unsigned int compact_second_chunk;
// parameters of block of intervals processed by this block of threads
__shared__ unsigned int c_block_start;
__shared__ unsigned int c_block_end;
__shared__ unsigned int c_block_offset_output;
// midpoint of currently active interval of the thread
float mid = 0.0f;
// number of eigenvalues smaller than \a mid
unsigned int mid_count = 0;
// current interval parameter
float left;
float right;
unsigned int left_count;
unsigned int right_count;
// helper for compaction, keep track which threads have a second child
unsigned int is_active_second = 0;
// initialize common start conditions
if (0 == tid) {
c_block_start = blocks_mult[blockIdx.x];
c_block_end = blocks_mult[blockIdx.x + 1];
c_block_offset_output = blocks_mult_sum[blockIdx.x];
num_threads_active = c_block_end - c_block_start;
s_compaction_list[0] = 0;
num_threads_compaction = ceilPow2(num_threads_active);
all_threads_converged = 1;
compact_second_chunk = 0;
}
cg::sync(cta);
// read data into shared memory
if (tid < num_threads_active) {
s_left[tid] = g_left[c_block_start + tid];
s_right[tid] = g_right[c_block_start + tid];
s_left_count[tid] = g_left_count[c_block_start + tid];
s_right_count[tid] = g_right_count[c_block_start + tid];
}
cg::sync(cta);
// do until all threads converged
while (true) {
// for (int iter=0; iter < 0; iter++) {
// subdivide interval if currently active and not already converged
subdivideActiveInterval(tid, s_left, s_right, s_left_count, s_right_count,
num_threads_active, left, right, left_count,
right_count, mid, all_threads_converged);
cg::sync(cta);
// stop if all eigenvalues have been found
if (1 == all_threads_converged) {
break;
}
// compute number of eigenvalues smaller than mid for active and not
// converged intervals, use all threads for loading data from gmem and
// s_left and s_right as scratch space to store the data load from gmem
// in shared memory
mid_count = computeNumSmallerEigenvalsLarge(g_d, g_s, n, mid, tid,
num_threads_active, s_left,
s_right, (left == right), cta);
cg::sync(cta);
if (tid < num_threads_active) {
// store intervals
if (left != right) {
storeNonEmptyIntervals(tid, num_threads_active, s_left, s_right,
s_left_count, s_right_count, left, mid, right,
left_count, mid_count, right_count, precision,
compact_second_chunk, s_compaction_list_exc,
is_active_second);
} else {
storeIntervalConverged(
s_left, s_right, s_left_count, s_right_count, left, mid, right,
left_count, mid_count, right_count, s_compaction_list_exc,
compact_second_chunk, num_threads_active, is_active_second);
}
}
cg::sync(cta);
// compact second chunk of intervals if any of the threads generated
// two child intervals
if (1 == compact_second_chunk) {
createIndicesCompaction(s_compaction_list_exc, num_threads_compaction,
cta);
compactIntervals(s_left, s_right, s_left_count, s_right_count, mid, right,
mid_count, right_count, s_compaction_list,
num_threads_active, is_active_second);
}
cg::sync(cta);
// update state variables
if (0 == tid) {
num_threads_active += s_compaction_list[num_threads_active];
num_threads_compaction = ceilPow2(num_threads_active);
compact_second_chunk = 0;
all_threads_converged = 1;
}
cg::sync(cta);
// clear
s_compaction_list_exc[threadIdx.x] = 0;
s_compaction_list_exc[threadIdx.x + blockDim.x] = 0;
cg::sync(cta);
} // end until all threads converged
// write data back to global memory
if (tid < num_threads_active) {
unsigned int addr = c_block_offset_output + tid;
g_lambda[addr] = s_left[tid];
g_pos[addr] = s_right_count[tid];
}
}
#endif // #ifndef _BISECT_KERNEL_LARGE_MULTI_H_