GPU 计算与并行编程
Valkyrie 提供强大的 GPU 计算能力，支持通用 GPU 编程 (GPGPU)，可以利用现代显卡的并行计算能力进行高性能计算任务。
计算着色器基础
工作组和线程模型
valkyrie
# 计算着色器的基本结构
@compute(workgroup_size = [64, 1, 1]) # 64个线程为一个工作组
micro basic_compute(id: ComputeInput) {
let thread_id = id.global_invocation_id.x
let local_id = id.local_invocation_id.x
let workgroup_id = id.workgroup_id.x
# 计算逻辑
process_data(thread_id)
}
# 2D 工作组示例
@compute(workgroup_size = [8, 8, 1]) # 8x8 = 64个线程
micro image_process_compute(id: ComputeInput) {
let coords = id.global_invocation_id.xy
let local_coords = id.local_invocation_id.xy
# 处理图像像素
process_pixel(coords)
}
# 3D 工作组示例
@compute(workgroup_size = [4, 4, 4]) # 4x4x4 = 64个线程
micro volume_compute(id: ComputeInput) {
let coords = id.global_invocation_id.xyz
# 处理体素数据
process_voxel(coords)
}存储缓冲区
valkyrie
# 只读存储缓冲区
@group(0) @binding(0)
let input_data: storage<array<f32>, read>
# 可读写存储缓冲区
@group(0) @binding(1)
let output_data: storage<array<f32>, read_write>
# 结构化数据
structure ParticleData {
position: Vec3,
velocity: Vec3,
mass: f32,
life: f32
}
@group(0) @binding(2)
let particles: storage<array<ParticleData>, read_write>
# Uniform 缓冲区（常量数据）
@group(1) @binding(0)
structure ComputeParams {
delta_time: f32,
particle_count: u32,
gravity: Vec3,
damping: f32
}并行算法实现
并行归约 (Parallel Reduction)
valkyrie
# 共享内存用于工作组内通信
@compute(workgroup_size = [256, 1, 1])
micro parallel_sum(id: ComputeInput) {
# 共享内存声明
var<workgroup> shared_data: array<f32, 256>
let thread_id = id.local_invocation_id.x
let global_id = id.global_invocation_id.x
# 加载数据到共享内存
if global_id < input_data.len() {
shared_data[thread_id] = input_data[global_id]
} else {
shared_data[thread_id] = 0.0
}
# 同步工作组内所有线程
workgroupBarrier()
# 并行归约
let mut stride = 128u32
while stride > 0 {
if thread_id < stride {
shared_data[thread_id] += shared_data[thread_id + stride]
}
workgroupBarrier()
stride /= 2
}
# 工作组的第一个线程写入结果
if thread_id == 0 {
let workgroup_id = id.workgroup_id.x
output_data[workgroup_id] = shared_data[0]
}
}
# 完整的归约实现
class ParallelReduction {
device: wgpu::Device,
queue: wgpu::Queue,
compute_pipeline: wgpu::ComputePipeline,
input_buffer: wgpu::Buffer,
output_buffer: wgpu::Buffer,
staging_buffer: wgpu::Buffer
new(device: wgpu::Device, queue: wgpu::Queue) -> ParallelReduction {
let shader = compile_valkyrie_shader(@include_str("parallel_sum.val"))
let compute_pipeline = create_compute_pipeline(device, shader)
ParallelReduction {
device,
queue,
compute_pipeline,
input_buffer: create_storage_buffer(device, 1024 * 1024 * 4), # 1M floats
output_buffer: create_storage_buffer(device, 4096 * 4), # 4K floats
staging_buffer: create_staging_buffer(device, 4096 * 4)
}
}
compute_sum(mut self, data: [f32]) -> f32 {
# 上传数据
self.queue.write_buffer(self.input_buffer, 0, bytemuck::cast_slice(data))
let mut encoder = self.device.create_command_encoder()
# 第一阶段：并行归约
{
let mut compute_pass = encoder.begin_compute_pass()
compute_pass.set_pipeline(self.compute_pipeline)
compute_pass.set_bind_group(0, bind_group, [])
let workgroups = (data.len() + 255) / 256
compute_pass.dispatch_workgroups(workgroups as u32, 1, 1)
}
# 复制结果到 staging buffer
encoder.copy_buffer_to_buffer(
self.output_buffer, 0,
self.staging_buffer, 0,
workgroups * 4
)
self.queue.submit([encoder.finish()])
# 读取结果
let slice = self.staging_buffer.slice(..(workgroups * 4))
let (sender, receiver) = futures_intrusive::channel::shared::oneshot_channel()
slice.map_async(wgpu::MapMode::Read, move |result| {
sender.send(result).unwrap()
})
self.device.poll(wgpu::Maintain::Wait)
receiver.receive().await.unwrap().unwrap()
let data = slice.get_mapped_range()
let result: [f32] = bytemuck::cast_slice(data)
# CPU 端完成最终归约
result.iter().sum()
}
}并行前缀和 (Parallel Prefix Sum)
valkyrie
# Blelloch 扫描算法实现
@compute(workgroup_size = [256, 1, 1])
micro prefix_sum_up_sweep(id: ComputeInput, params: ScanParams) {
var<workgroup> shared_data: array<u32, 512> # 双倍大小用于 padding
let thread_id = id.local_invocation_id.x
let global_id = id.global_invocation_id.x
# 加载数据
let ai = thread_id
let bi = thread_id + 256
shared_data[ai] = if global_id < input_data.len() { input_data[global_id] } else { 0 }
shared_data[bi] = if global_id + 256 < input_data.len() { input_data[global_id + 256] } else { 0 }
# Up-sweep 阶段
let mut offset = 1u32
let mut d = 256u32
while d > 0 {
workgroupBarrier()
if thread_id < d {
let ai = offset * (2 * thread_id + 1) - 1
let bi = offset * (2 * thread_id + 2) - 1
shared_data[bi] += shared_data[ai]
}
offset *= 2
d /= 2
}
# 清零最后一个元素
if thread_id == 0 {
shared_data[511] = 0
}
# Down-sweep 阶段
d = 1
while d < 512 {
offset /= 2
workgroupBarrier()
if thread_id < d {
let ai = offset * (2 * thread_id + 1) - 1
let bi = offset * (2 * thread_id + 2) - 1
let temp = shared_data[ai]
shared_data[ai] = shared_data[bi]
shared_data[bi] += temp
}
d *= 2
}
workgroupBarrier()
# 写回结果
if global_id < output_data.len() {
output_data[global_id] = shared_data[ai]
}
if global_id + 256 < output_data.len() {
output_data[global_id + 256] = shared_data[bi]
}
}并行排序
valkyrie
# 双调排序 (Bitonic Sort)
@compute(workgroup_size = [256, 1, 1])
micro bitonic_sort(id: ComputeInput, params: SortParams) {
var<workgroup> shared_data: array<u32, 256>
let thread_id = id.local_invocation_id.x
let global_id = id.global_invocation_id.x
# 加载数据
shared_data[thread_id] = if global_id < input_data.len() {
input_data[global_id]
} else {
0xFFFFFFFF # 最大值作为填充
}
workgroupBarrier()
# 双调排序主循环
let mut k = 2u32
while k <= 256 {
let mut j = k / 2
while j > 0 {
let ixj = thread_id ^ j
if ixj > thread_id {
let ascending = (thread_id & k) == 0
if (shared_data[thread_id] > shared_data[ixj]) == ascending {
# 交换元素
let temp = shared_data[thread_id]
shared_data[thread_id] = shared_data[ixj]
shared_data[ixj] = temp
}
}
workgroupBarrier()
j /= 2
}
k *= 2
}
# 写回结果
if global_id < output_data.len() {
output_data[global_id] = shared_data[thread_id]
}
}
# 基数排序实现
@compute(workgroup_size = [256, 1, 1])
micro radix_sort_count(id: ComputeInput, params: RadixSortParams) {
var<workgroup> local_histogram: array<u32, 16> # 4-bit 基数
let thread_id = id.local_invocation_id.x
let global_id = id.global_invocation_id.x
# 初始化本地直方图
if thread_id < 16 {
local_histogram[thread_id] = 0
}
workgroupBarrier()
# 计算本地直方图
if global_id < input_data.len() {
let value = input_data[global_id]
let digit = (value >> (params.bit_shift * 4)) & 0xF
atomicAdd(local_histogram[digit], 1u)
}
workgroupBarrier()
# 将本地直方图写入全局直方图
if thread_id < 16 {
let workgroup_id = id.workgroup_id.x
global_histogram[workgroup_id * 16 + thread_id] = local_histogram[thread_id]
}
}物理模拟
N-Body 粒子模拟
valkyrie
# N-Body 重力模拟
structure Body {
position: Vec3,
velocity: Vec3,
mass: f32,
_padding: f32 # 对齐到 16 字节
}
@group(0) @binding(0)
let bodies: storage<array<Body>, read_write>
@group(0) @binding(1)
structure NBodyParams {
body_count: u32,
delta_time: f32,
softening: f32, # 软化参数避免奇点
damping: f32
}
@compute(workgroup_size = [64, 1, 1])
micro n_body_simulation(id: ComputeInput, params: NBodyParams) {
let index = id.global_invocation_id.x
if index >= params.body_count {
return
}
let body = bodies[index]
let mut force = Vec3(0.0, 0.0, 0.0)
# 计算所有其他物体的引力
for i in 0..params.body_count {
if i != index {
let other = bodies[i]
let r = other.position - body.position
let dist_sq = dot(r, r) + params.softening * params.softening
let dist = sqrt(dist_sq)
let force_magnitude = other.mass / (dist_sq * dist)
force += r * force_magnitude
}
}
# 更新速度和位置
let acceleration = force / body.mass
let new_velocity = body.velocity + acceleration * params.delta_time
let new_position = body.position + new_velocity * params.delta_time
# 应用阻尼
let damped_velocity = new_velocity * params.damping
# 写回结果
bodies[index].velocity = damped_velocity
bodies[index].position = new_position
}
# 优化版本：使用共享内存
@compute(workgroup_size = [64, 1, 1])
micro n_body_optimized(id: ComputeInput, params: NBodyParams) {
var<workgroup> shared_bodies: array<Body, 64>
let thread_id = id.local_invocation_id.x
let global_id = id.global_invocation_id.x
if global_id >= params.body_count {
return
}
let body = bodies[global_id]
let mut force = Vec3(0.0, 0.0, 0.0)
# 分块处理
let num_tiles = (params.body_count + 63) / 64
for tile in 0..num_tiles {
let tile_start = tile * 64
let shared_index = tile_start + thread_id
# 加载一个 tile 的数据到共享内存
if shared_index < params.body_count {
shared_bodies[thread_id] = bodies[shared_index]
}
workgroupBarrier()
# 计算与当前 tile 中所有物体的相互作用
let tile_size = min(64u32, params.body_count - tile_start)
for i in 0..tile_size {
if tile_start + i != global_id {
let other = shared_bodies[i]
let r = other.position - body.position
let dist_sq = dot(r, r) + params.softening * params.softening
let dist = sqrt(dist_sq)
let force_magnitude = other.mass / (dist_sq * dist)
force += r * force_magnitude
}
}
workgroupBarrier()
}
# 更新物体状态
let acceleration = force / body.mass
bodies[global_id].velocity += acceleration * params.delta_time
bodies[global_id].position += bodies[global_id].velocity * params.delta_time
bodies[global_id].velocity *= params.damping
}流体模拟 (SPH)
valkyrie
# 光滑粒子流体动力学 (Smoothed Particle Hydrodynamics)
structure FluidParticle {
position: Vec3,
velocity: Vec3,
density: f32,
pressure: f32,
force: Vec3,
_padding: f32
}
@group(0) @binding(0)
let particles: storage<array<FluidParticle>, read_write>
@group(0) @binding(1)
structure SPHParams {
particle_count: u32,
rest_density: f32,
gas_constant: f32,
viscosity: f32,
smoothing_radius: f32,
particle_mass: f32,
delta_time: f32,
gravity: Vec3
}
# SPH 核函数
micro poly6_kernel(r: f32, h: f32) -> f32 {
if r >= h {
return 0.0
}
let h2 = h * h
let h9 = h2 * h2 * h2 * h2 * h
let r2 = r * r
315.0 / (64.0 * PI * h9) * pow(h2 - r2, 3.0)
}
micro spiky_gradient_kernel(r_vec: Vec3, h: f32) -> Vec3 {
let r = length(r_vec)
if r >= h || r == 0.0 {
return Vec3(0.0, 0.0, 0.0)
}
let h6 = h * h * h * h * h * h
let factor = -45.0 / (PI * h6) * pow(h - r, 2.0) / r
r_vec * factor
}
micro viscosity_laplacian_kernel(r: f32, h: f32) -> f32 {
if r >= h {
return 0.0
}
let h6 = h * h * h * h * h * h
45.0 / (PI * h6) * (h - r)
}
# 密度计算
@compute(workgroup_size = [64, 1, 1])
micro compute_density(id: ComputeInput, params: SPHParams) {
let index = id.global_invocation_id.x
if index >= params.particle_count {
return
}
let particle = particles[index]
let mut density = 0.0
# 计算密度
for i in 0..params.particle_count {
let other = particles[i]
let r = length(particle.position - other.position)
if r < params.smoothing_radius {
density += params.particle_mass * poly6_kernel(r, params.smoothing_radius)
}
}
particles[index].density = density
particles[index].pressure = params.gas_constant * (density - params.rest_density)
}
# 力计算
@compute(workgroup_size = [64, 1, 1])
micro compute_forces(id: ComputeInput, params: SPHParams) {
let index = id.global_invocation_id.x
if index >= params.particle_count {
return
}
let particle = particles[index]
let mut pressure_force = Vec3(0.0, 0.0, 0.0)
let mut viscosity_force = Vec3(0.0, 0.0, 0.0)
for i in 0..params.particle_count {
if i == index {
continue
}
let other = particles[i]
let r_vec = particle.position - other.position
let r = length(r_vec)
if r < params.smoothing_radius && r > 0.0 {
# 压力力
let pressure_gradient = spiky_gradient_kernel(r_vec, params.smoothing_radius)
pressure_force -= params.particle_mass *
(particle.pressure + other.pressure) / (2.0 * other.density) *
pressure_gradient
# 粘性力
let velocity_diff = other.velocity - particle.velocity
let viscosity_laplacian = viscosity_laplacian_kernel(r, params.smoothing_radius)
viscosity_force += params.viscosity * params.particle_mass *
velocity_diff / other.density * viscosity_laplacian
}
}
# 总力 = 压力力 + 粘性力 + 重力
let total_force = pressure_force + viscosity_force + params.gravity * particle.density
particles[index].force = total_force
}
# 积分更新
@compute(workgroup_size = [64, 1, 1])
micro integrate_particles(id: ComputeInput, params: SPHParams) {
let index = id.global_invocation_id.x
if index >= params.particle_count {
return
}
let mut particle = particles[index]
# Leapfrog 积分
let acceleration = particle.force / particle.density
particle.velocity += acceleration * params.delta_time
particle.position += particle.velocity * params.delta_time
# 边界条件（简单反弹）
let boundary = 10.0
if particle.position.x < -boundary || particle.position.x > boundary {
particle.velocity.x *= -0.8
particle.position.x = clamp(particle.position.x, -boundary, boundary)
}
if particle.position.y < -boundary || particle.position.y > boundary {
particle.velocity.y *= -0.8
particle.position.y = clamp(particle.position.y, -boundary, boundary)
}
if particle.position.z < -boundary || particle.position.z > boundary {
particle.velocity.z *= -0.8
particle.position.z = clamp(particle.position.z, -boundary, boundary)
}
particles[index] = particle
}机器学习加速
矩阵乘法
valkyrie
# 高效的矩阵乘法实现
@compute(workgroup_size = [16, 16, 1])
micro matrix_multiply(id: ComputeInput, params: MatMulParams) {
var<workgroup> tile_a: array<array<f32, 16>, 16>
var<workgroup> tile_b: array<array<f32, 16>, 16>
let row = id.global_invocation_id.y
let col = id.global_invocation_id.x
let local_row = id.local_invocation_id.y
let local_col = id.local_invocation_id.x
if row >= params.m || col >= params.n {
return
}
let mut sum = 0.0
let num_tiles = (params.k + 15) / 16
for tile in 0..num_tiles {
let tile_start = tile * 16
# 加载 A 的 tile
let a_col = tile_start + local_col
if row < params.m && a_col < params.k {
tile_a[local_row][local_col] = matrix_a[row * params.k + a_col]
} else {
tile_a[local_row][local_col] = 0.0
}
# 加载 B 的 tile
let b_row = tile_start + local_row
if b_row < params.k && col < params.n {
tile_b[local_row][local_col] = matrix_b[b_row * params.n + col]
} else {
tile_b[local_row][local_col] = 0.0
}
workgroupBarrier()
# 计算部分乘积
for k in 0..16 {
sum += tile_a[local_row][k] * tile_b[k][local_col]
}
workgroupBarrier()
}
# 写入结果
matrix_c[row * params.n + col] = sum
}
# 批量矩阵乘法
@compute(workgroup_size = [8, 8, 4])
micro batch_matrix_multiply(id: ComputeInput, params: BatchMatMulParams) {
let batch = id.global_invocation_id.z
let row = id.global_invocation_id.y
let col = id.global_invocation_id.x
if batch >= params.batch_size || row >= params.m || col >= params.n {
return
}
let mut sum = 0.0
let a_offset = batch * params.m * params.k
let b_offset = batch * params.k * params.n
let c_offset = batch * params.m * params.n
for k in 0..params.k {
let a_val = matrix_a[a_offset + row * params.k + k]
let b_val = matrix_b[b_offset + k * params.n + col]
sum += a_val * b_val
}
matrix_c[c_offset + row * params.n + col] = sum
}卷积神经网络
valkyrie
# 2D 卷积层
@compute(workgroup_size = [8, 8, 1])
micro conv2d(id: ComputeInput, params: Conv2DParams) {
let out_y = id.global_invocation_id.y
let out_x = id.global_invocation_id.x
let out_c = id.global_invocation_id.z
if out_y >= params.output_height || out_x >= params.output_width || out_c >= params.output_channels {
return
}
let mut sum = 0.0
# 卷积计算
for in_c in 0..params.input_channels {
for ky in 0..params.kernel_size {
for kx in 0..params.kernel_size {
let in_y = out_y * params.stride + ky - params.padding
let in_x = out_x * params.stride + kx - params.padding
if in_y >= 0 && in_y < params.input_height &&
in_x >= 0 && in_x < params.input_width {
let input_idx = in_c * params.input_height * params.input_width +
in_y * params.input_width + in_x
let weight_idx = out_c * params.input_channels * params.kernel_size * params.kernel_size +
in_c * params.kernel_size * params.kernel_size +
ky * params.kernel_size + kx
sum += input_data[input_idx] * weights[weight_idx]
}
}
}
}
# 添加偏置并应用激活函数
sum += biases[out_c]
let activated = relu(sum) # ReLU 激活
let output_idx = out_c * params.output_height * params.output_width +
out_y * params.output_width + out_x
output_data[output_idx] = activated
}
# 激活函数
micro relu(x: f32) -> f32 {
max(0.0, x)
}
micro sigmoid(x: f32) -> f32 {
1.0 / (1.0 + exp(-x))
}
micro tanh_activation(x: f32) -> f32 {
tanh(x)
}
micro gelu(x: f32) -> f32 {
0.5 * x * (1.0 + tanh(sqrt(2.0 / PI) * (x + 0.044715 * pow(x, 3.0))))
}
# 批量归一化
@compute(workgroup_size = [256, 1, 1])
micro batch_norm(id: ComputeInput, params: BatchNormParams) {
let index = id.global_invocation_id.x
if index >= params.size {
return
}
let channel = index % params.channels
let mean = running_mean[channel]
let var = running_var[channel]
let gamma = scale[channel]
let beta = shift[channel]
let normalized = (input_data[index] - mean) / sqrt(var + params.epsilon)
output_data[index] = gamma * normalized + beta
}图像处理
高级滤波器
valkyrie
# 双边滤波器
@compute(workgroup_size = [8, 8, 1])
micro bilateral_filter(id: ComputeInput, params: BilateralParams) {
let coords = id.global_invocation_id.xy
let dimensions = textureDimensions(input_texture)
if coords.x >= dimensions.x || coords.y >= dimensions.y {
return
}
let center_color = textureLoad(input_texture, coords, 0)
let mut weighted_sum = Vec3(0.0, 0.0, 0.0)
let mut weight_sum = 0.0
let radius = params.radius
let sigma_space = params.sigma_space
let sigma_color = params.sigma_color
for dy in -radius..=radius {
for dx in -radius..=radius {
let sample_coords = coords + Vec2(dx, dy)
if sample_coords.x >= 0 && sample_coords.x < dimensions.x &&
sample_coords.y >= 0 && sample_coords.y < dimensions.y {
let sample_color = textureLoad(input_texture, sample_coords, 0)
# 空间权重
let spatial_dist = sqrt((dx * dx + dy * dy) as f32)
let spatial_weight = exp(-(spatial_dist * spatial_dist) / (2.0 * sigma_space * sigma_space))
# 颜色权重
let color_dist = length(sample_color.rgb - center_color.rgb)
let color_weight = exp(-(color_dist * color_dist) / (2.0 * sigma_color * sigma_color))
let weight = spatial_weight * color_weight
weighted_sum += sample_color.rgb * weight
weight_sum += weight
}
}
}
let filtered_color = if weight_sum > 0.0 {
weighted_sum / weight_sum
} else {
center_color.rgb
}
textureStore(output_texture, coords, Vec4(filtered_color, center_color.a))
}
# 非局部均值去噪
@compute(workgroup_size = [8, 8, 1])
micro non_local_means(id: ComputeInput, params: NLMParams) {
let coords = id.global_invocation_id.xy
let dimensions = textureDimensions(input_texture)
if coords.x >= dimensions.x || coords.y >= dimensions.y {
return
}
let patch_size = params.patch_size
let search_window = params.search_window
let h = params.filtering_parameter
let mut weighted_sum = Vec3(0.0, 0.0, 0.0)
let mut weight_sum = 0.0
# 搜索窗口
for sy in -search_window..=search_window {
for sx in -search_window..=search_window {
let search_coords = coords + Vec2(sx, sy)
if search_coords.x >= patch_size && search_coords.x < dimensions.x - patch_size &&
search_coords.y >= patch_size && search_coords.y < dimensions.y - patch_size {
# 计算补丁相似度
let mut patch_distance = 0.0
let mut patch_count = 0
for py in -patch_size..=patch_size {
for px in -patch_size..=patch_size {
let p1 = textureLoad(input_texture, coords + Vec2(px, py), 0)
let p2 = textureLoad(input_texture, search_coords + Vec2(px, py), 0)
let diff = p1.rgb - p2.rgb
patch_distance += dot(diff, diff)
patch_count += 1
}
}
patch_distance /= patch_count as f32
# 计算权重
let weight = exp(-max(patch_distance - 2.0 * params.noise_variance, 0.0) / (h * h))
let sample_color = textureLoad(input_texture, search_coords, 0)
weighted_sum += sample_color.rgb * weight
weight_sum += weight
}
}
}
let denoised_color = if weight_sum > 0.0 {
weighted_sum / weight_sum
} else {
textureLoad(input_texture, coords, 0).rgb
}
textureStore(output_texture, coords, Vec4(denoised_color, 1.0))
}性能优化技巧
内存访问优化
valkyrie
# 合并内存访问
@compute(workgroup_size = [32, 1, 1]) # warp size
micro coalesced_access(id: ComputeInput) {
let thread_id = id.global_invocation_id.x
# 好的访问模式：连续访问
let value = input_data[thread_id] # 线程 i 访问元素 i
# 避免的访问模式：跨步访问
# let value = input_data[thread_id * stride] # 可能导致内存带宽浪费
output_data[thread_id] = process(value)
}
# 使用共享内存减少全局内存访问
@compute(workgroup_size = [256, 1, 1])
micro shared_memory_optimization(id: ComputeInput) {
var<workgroup> shared_cache: array<f32, 256>
let thread_id = id.local_invocation_id.x
let global_id = id.global_invocation_id.x
# 协作加载数据到共享内存
shared_cache[thread_id] = input_data[global_id]
workgroupBarrier()
# 现在可以快速访问共享内存中的数据
let mut result = 0.0
for i in 0..256 {
result += shared_cache[i] * coefficients[i]
}
output_data[global_id] = result
}分支优化
valkyrie
# 避免分支分歧
@compute(workgroup_size = [32, 1, 1])
micro branch_optimization(id: ComputeInput) {
let thread_id = id.global_invocation_id.x
let value = input_data[thread_id]
# 不好的分支：可能导致 warp 分歧
# if thread_id % 2 == 0 {
# result = expensive_computation_a(value)
# } else {
# result = expensive_computation_b(value)
# }
# 更好的方法：使用条件表达式
let condition = thread_id % 2 == 0
let result_a = expensive_computation_a(value)
let result_b = expensive_computation_b(value)
let result = if condition { result_a } else { result_b }
# 或者重新组织算法避免分支
output_data[thread_id] = result
}占用率优化
valkyrie
# 优化寄存器使用
@compute(workgroup_size = [128, 1, 1]) # 调整工作组大小以平衡占用率
micro register_optimization(id: ComputeInput) {
let thread_id = id.global_invocation_id.x
# 避免使用过多局部变量（寄存器）
# 将大数组移到共享内存或重新计算
let value = input_data[thread_id]
let result = complex_computation(value) # 内联小函数
output_data[thread_id] = result
}
# 内存带宽优化
@compute(workgroup_size = [64, 1, 1])
micro bandwidth_optimization(id: ComputeInput) {
let thread_id = id.global_invocation_id.x
# 向量化访问
let vec4_index = thread_id / 4
let vec4_data = input_vec4[vec4_index] # 一次读取 4 个 f32
# 处理向量数据
let processed = process_vec4(vec4_data)
output_vec4[vec4_index] = processed
}调试和性能分析
GPU 调试工具
valkyrie
# 调试缓冲区
@group(2) @binding(0)
let debug_buffer: storage<array<DebugInfo>, write>
structure DebugInfo {
thread_id: u32,
value: f32,
iteration: u32,
timestamp: u32
}
@compute(workgroup_size = [64, 1, 1])
micro debug_compute(id: ComputeInput) {
let thread_id = id.global_invocation_id.x
# 记录调试信息
debug_buffer[thread_id] = DebugInfo {
thread_id: thread_id,
value: input_data[thread_id],
iteration: 0,
timestamp: 0 # GPU 时间戳
}
# 主要计算逻辑
let result = compute_something(input_data[thread_id])
# 更新调试信息
debug_buffer[thread_id].value = result
debug_buffer[thread_id].iteration = 1
output_data[thread_id] = result
}
# 性能计数器
class GPUPerformanceCounter {
query_sets: [wgpu::QuerySet],
timestamp_period: f32
new(device: wgpu::Device) -> GPUPerformanceCounter {
let timestamp_query = device.create_query_set(wgpu::QuerySetDescriptor {
label: Some("Timestamp Queries"),
ty: wgpu::QueryType::Timestamp,
count: 64
})
GPUPerformanceCounter {
query_sets: [timestamp_query],
timestamp_period: queue.get_timestamp_period()
}
}
measure_compute_time(self, encoder: wgpu::CommandEncoder, compute_pass: imply FnOnce()) -> f64 {
encoder.write_timestamp(self.query_sets[0], 0)
compute_pass()
encoder.write_timestamp(self.query_sets[0], 1)
# 解析时间戳并返回毫秒
let timestamps = self.resolve_timestamps()
(timestamps[1] - timestamps[0]) as f64 * self.timestamp_period as f64 / 1_000_000.0
}
}总结
Valkyrie 的 GPU 计算能力提供了：
- 现代并行编程模型 - 支持计算着色器和 GPGPU 编程
- 高性能算法实现 - 并行归约、排序、前缀和等基础算法
- 物理模拟加速 - N-Body、SPH 流体模拟等复杂物理计算
- 机器学习支持 - 矩阵运算、卷积神经网络等 AI 计算
- 图像处理优化 - 高级滤波器和图像算法的 GPU 实现
- 性能优化工具 - 内存访问优化、分支优化、调试工具等
通过 Valkyrie 的 GPU 计算框架，开发者可以充分利用现代显卡的并行计算能力，实现高性能的科学计算、游戏物理、机器学习和图像处理应用。