/*
* wavefront_optimized_matmul.hip
* Advanced matrix multiplication optimized for AMD GPU architecture
* Leverages wavefront primitives, LDS memory, and GCN/RDNA instruction set
* Target: GCN 3.0+ and RDNA architecture (RX 580, RX 6000/7000 series, MI100/MI200)
*/
#include <hip/hip_runtime.h>
#include <hip/hip_cooperative_groups.h>
// Enable wavefront intrinsics for AMD GPUs
#ifdef __HIP_PLATFORM_AMD__
#include <hip/amd_detail/amd_hip_intrinsics.h>
#endif
using namespace cooperative_groups;
// AMD GPU architecture-specific constants
constexpr int WAVEFRONT_SIZE = 64; // AMD wavefront size
constexpr int TILE_SIZE = 64; // Optimal tile size for GCN/RDNA
constexpr int LDS_SIZE = 32768; // Local Data Share size per compute unit
// Wavefront-level matrix multiplication for AMD GPUs
// Optimized for RDNA architecture with enhanced wave32 support
__global__ void wavefront_gemm_f32(const float* __restrict__ A,
const float* __restrict__ B,
float* __restrict__ C,
int M, int N, int K,
int lda, int ldb, int ldc) {
// Local Data Share (LDS) for cooperative tile caching
__shared__ float tile_A[TILE_SIZE][TILE_SIZE + 1]; // +1 to avoid bank conflicts
__shared__ float tile_B[TILE_SIZE][TILE_SIZE + 1];
// Wavefront and thread identification
int wavefront_id = threadIdx.x / WAVEFRONT_SIZE;
int lane_id = threadIdx.x % WAVEFRONT_SIZE;
int wave_row = lane_id / 8; // 8x8 threads per wavefront tile
int wave_col = lane_id % 8;
// Block coordinates
int block_row = blockIdx.x * TILE_SIZE;
int block_col = blockIdx.y * TILE_SIZE;
// Local accumulator for this thread (4x4 sub-tile)
float4 acc[4] = {{0.0f, 0.0f, 0.0f, 0.0f},
{0.0f, 0.0f, 0.0f, 0.0f},
{0.0f, 0.0f, 0.0f, 0.0f},
{0.0f, 0.0f, 0.0f, 0.0f}};
// Main computation loop over K dimension
for (int k_tile = 0; k_tile < K; k_tile += TILE_SIZE) {
// Cooperative loading of A tile using wavefront coordination
// Each wavefront loads a 8x8 sub-tile cooperatively
int A_row = block_row + wavefront_id * 8 + wave_row;
int A_col = k_tile + wave_col * 8;
// Vectorized loading with boundary checking
if (A_row < M && A_col + 7 < K) {
// Use global memory coalescing with float4 loads
float4 A_vec1 = reinterpret_cast<const float4*>(&A[A_row * lda + A_col])[0];
float4 A_vec2 = reinterpret_cast<const float4*>(&A[A_row * lda + A_col + 4])[0];
// Store in LDS with proper indexing
int lds_row = wavefront_id * 8 + wave_row;
int lds_col = wave_col * 8;
tile_A[lds_row][lds_col + 0] = A_vec1.x;
tile_A[lds_row][lds_col + 1] = A_vec1.y;
tile_A[lds_row][lds_col + 2] = A_vec1.z;
tile_A[lds_row][lds_col + 3] = A_vec1.w;
tile_A[lds_row][lds_col + 4] = A_vec2.x;
tile_A[lds_row][lds_col + 5] = A_vec2.y;
tile_A[lds_row][lds_col + 6] = A_vec2.z;
tile_A[lds_row][lds_col + 7] = A_vec2.w;
}
// Cooperative loading of B tile
int B_row = k_tile + wavefront_id * 8 + wave_row;
int B_col = block_col + wave_col * 8;
if (B_row < K && B_col + 7 < N) {
float4 B_vec1 = reinterpret_cast<const float4*>(&B[B_row * ldb + B_col])[0];
float4 B_vec2 = reinterpret_cast<const float4*>(&B[B_row * ldb + B_col + 4])[0];
int lds_row = wavefront_id * 8 + wave_row;
int lds_col = wave_col * 8;
tile_B[lds_row][lds_col + 0] = B_vec1.x;
tile_B[lds_row][lds_col + 1] = B_vec1.y;
tile_B[lds_row][lds_col + 2] = B_vec1.z;
tile_B[lds_row][lds_col + 3] = B_vec1.w;
tile_B[lds_row][lds_col + 4] = B_vec2.x;
tile_B[lds_row][lds_col + 5] = B_vec2.y;
tile_B[lds_row][lds_col + 6] = B_vec2.z;
tile_B[lds_row][lds_col + 7] = B_vec2.w;
}
// Synchronize workgroup before computation
__syncthreads();
// Inner product computation using LDS data
// Each thread computes a 4x4 sub-tile
for (int k_inner = 0; k_inner < TILE_SIZE; k_inner++) {
// Load 4 elements from A tile
float4 A_fragment = {
tile_A[wavefront_id * 8 + wave_row * 4 + 0][k_inner],
tile_A[wavefront_id * 8 + wave_row * 4 + 1][k_inner],
tile_A[wavefront_id * 8 + wave_row * 4 + 2][k_inner],
tile_A[wavefront_id * 8 + wave_row * 4 + 3][k_inner]
};
// Load 4 elements from B tile
float4 B_fragment = {
tile_B[k_inner][wave_col * 4 + 0],
tile_B[k_inner][wave_col * 4 + 1],
tile_B[k_inner][wave_col * 4 + 2],
tile_B[k_inner][wave_col * 4 + 3]
};
// Perform outer product using fused multiply-add
acc[0].x = __fmaf_rn(A_fragment.x, B_fragment.x, acc[0].x);
acc[0].y = __fmaf_rn(A_fragment.x, B_fragment.y, acc[0].y);
acc[0].z = __fmaf_rn(A_fragment.x, B_fragment.z, acc[0].z);
acc[0].w = __fmaf_rn(A_fragment.x, B_fragment.w, acc[0].w);
acc[1].x = __fmaf_rn(A_fragment.y, B_fragment.x, acc[1].x);
acc[1].y = __fmaf_rn(A_fragment.y, B_fragment.y, acc[1].y);
acc[1].z = __fmaf_rn(A_fragment.y, B_fragment.z, acc[1].z);
acc[1].w = __fmaf_rn(A_fragment.y, B_fragment.w, acc[1].w);
acc[2].x = __fmaf_rn(A_fragment.z, B_fragment.x, acc[2].x);
acc[2].y = __fmaf_rn(A_fragment.z, B_fragment.y, acc[2].y);
acc[2].z = __fmaf_rn(A_fragment.z, B_fragment.z, acc[2].z);
acc[2].w = __fmaf_rn(A_fragment.z, B_fragment.w, acc[2].w);
acc[3].x = __fmaf_rn(A_fragment.w, B_fragment.x, acc[3].x);
acc[3].y = __fmaf_rn(A_fragment.w, B_fragment.y, acc[3].y);
acc[3].z = __fmaf_rn(A_fragment.w, B_fragment.z, acc[3].z);
acc[3].w = __fmaf_rn(A_fragment.w, B_fragment.w, acc[3].w);
}
// Synchronize before next tile
__syncthreads();
}
// Write results back to global memory using vectorized stores
int C_row = block_row + wavefront_id * 8 + wave_row * 4;
int C_col = block_col + wave_col * 4;
if (C_row + 3 < M && C_col + 3 < N) {
// Store 4x4 sub-tile using coalesced memory access
reinterpret_cast<float4*>(&C[(C_row + 0) * ldc + C_col])[0] = acc[0];
reinterpret_cast<float4*>(&C[(C_row + 1) * ldc + C_col])[0] = acc[1];
reinterpret_cast<float4*>(&C[(C_row + 2) * ldc + C_col])[0] = acc[2];
reinterpret_cast<float4*>(&C[(C_row + 3) * ldc + C_col])[0] = acc[3];
}
}
// Half precision variant for memory bandwidth optimization
__global__ void wavefront_gemm_f16(const __half* __restrict__ A,
const __half* __restrict__ B,
__half* __restrict__ C,
int M, int N, int K,
int lda, int ldb, int ldc) {
// Use larger tiles for half precision due to doubled memory density
constexpr int HALF_TILE_SIZE = 128;
__shared__ __half tile_A[HALF_TILE_SIZE][HALF_TILE_SIZE + 8]; // Padding for alignment
__shared__ __half tile_B[HALF_TILE_SIZE][HALF_TILE_SIZE + 8];
int wavefront_id = threadIdx.x / WAVEFRONT_SIZE;
int lane_id = threadIdx.x % WAVEFRONT_SIZE;
// Use half8 for maximum vectorization
half8 acc[4] = {{0, 0, 0, 0, 0, 0, 0, 0},
{0, 0, 0, 0, 0, 0, 0, 0},
{0, 0, 0, 0, 0, 0, 0, 0},
{0, 0, 0, 0, 0, 0, 0, 0}};
// Computation loop optimized for half precision
for (int k_tile = 0; k_tile < K; k_tile += HALF_TILE_SIZE) {
// Load tiles cooperatively using half8 vectorization
// ... similar structure to f32 version but using half8 operations
__syncthreads();
// Inner computation using half precision fused operations
for (int k_inner = 0; k_inner < HALF_TILE_SIZE; k_inner++) {
// Half precision matrix multiply using __hfma2 for 2-element operations
}
__syncthreads();
}
// Store results using half8 vectorized writes
}
// AMD RDNA-optimized convolution kernel
__global__ void wavefront_conv2d_optimized(const float* __restrict__ input,
const float* __restrict__ weights,
float* __restrict__ output,
int batch_size, int in_channels, int out_channels,
int input_height, int input_width,
int kernel_height, int kernel_width,
int stride, int padding) {
// Use LDS for input and weight caching
extern __shared__ float shared_memory[];
float* shared_input = shared_memory;
float* shared_weights = shared_memory + TILE_SIZE * TILE_SIZE;
int wavefront_id = threadIdx.x / WAVEFRONT_SIZE;
int lane_id = threadIdx.x % WAVEFRONT_SIZE;
// Calculate output coordinates
int out_y = blockIdx.x * TILE_SIZE + (lane_id / 8);
int out_x = blockIdx.y * TILE_SIZE + (lane_id % 8);
int out_c = blockIdx.z;
float accumulator = 0.0f;
// Process input channels in chunks for optimal LDS usage
for (int in_c_base = 0; in_c_base < in_channels; in_c_base += 16) {
// Cooperative loading of input patch to LDS
// ... wavefront-coordinated loading
// Load weight tiles cooperatively
// ... optimized weight loading
__syncthreads();
// Convolution computation using LDS data
for (int ky = 0; ky < kernel_height; ky++) {
for (int kx = 0; kx < kernel_width; kx++) {
for (int ic = 0; ic < 16 && in_c_base + ic < in_channels; ic++) {
int in_y = out_y * stride + ky - padding;
int in_x = out_x * stride + kx - padding;
if (in_y >= 0 && in_y < input_height &&
in_x >= 0 && in_x < input_width) {
float input_val = shared_input[(in_y % TILE_SIZE) * TILE_SIZE +
(in_x % TILE_SIZE)];
float weight_val = shared_weights[ky * kernel_width * 16 +
kx * 16 + ic];
accumulator = __fmaf_rn(input_val, weight_val, accumulator);
}
}
}
}
__syncthreads();
}
// Store result with wavefront coordination
if (out_y < input_height && out_x < input_width) {
int output_idx = out_y * input_width * out_channels +
out_x * out_channels + out_c;
output[output_idx] = accumulator;
}
}
// Wavefront-level reduction using AMD's ballot and shuffle intrinsics
__global__ void wavefront_reduction(const float* __restrict__ input,
float* __restrict__ output,
int count) {
int tid = blockIdx.x * blockDim.x + threadIdx.x;
int lane_id = threadIdx.x % WAVEFRONT_SIZE;
int wavefront_id = threadIdx.x / WAVEFRONT_SIZE;
float local_sum = 0.0f;
// Each thread processes multiple elements
for (int i = tid; i < count; i += gridDim.x * blockDim.x) {
local_sum += input[i];
}
#ifdef __HIP_PLATFORM_AMD__
// Use AMD-specific wavefront reduction primitives
local_sum = __shfl_down(local_sum, 32);
local_sum = __shfl_down(local_sum, 16);
local_sum = __shfl_down(local_sum, 8);
local_sum = __shfl_down(local_sum, 4);
local_sum = __shfl_down(local_sum, 2);
local_sum = __shfl_down(local_sum, 1);
#endif
// First thread in each wavefront writes partial result
if (lane_id == 0) {
atomicAdd(&output[0], local_sum);
}
}
// Wavefront-optimized transpose for memory-bound operations
__global__ void wavefront_transpose_coalesced(const float* __restrict__ input,
float* __restrict__ output,
int rows, int cols) {
__shared__ float tile[TILE_SIZE][TILE_SIZE + 1]; // Bank conflict avoidance
int in_row = blockIdx.x * TILE_SIZE + threadIdx.x;
int in_col = blockIdx.y * TILE_SIZE + threadIdx.y;
// Cooperative loading with wavefront coordination
if (in_row < rows && in_col < cols) {
tile[threadIdx.x][threadIdx.y] = input[in_row * cols + in_col];
}
__syncthreads();
// Transposed write coordinates
int out_row = blockIdx.y * TILE_SIZE + threadIdx.x;
int out_col = blockIdx.x * TILE_SIZE + threadIdx.y;
if (out_row < cols && out_col < rows) {
output[out_row * rows + out_col] = tile[threadIdx.y][threadIdx.x];
}
}