File size: 3,842 Bytes
e92f76f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 | use std::collections::HashMap;
// --- [LAYER 1: PyTorch/CuTe Layouts] ---
// Models how a tensor is logically mapped to physical memory offsets
#[derive(Debug, Clone)]
struct CuTeLayout {
shape: Vec<usize>,
stride: Vec<usize>,
}
impl CuTeLayout {
fn get_offset(&self, coords: &[usize]) -> usize {
coords.iter().zip(&self.stride).map(|(c, s)| c * s).sum()
}
}
// --- [LAYER 2: PTX / SASS ISA] ---
// Represents the machine instructions that trigger Tensor Core hardware
#[derive(Debug, Clone)]
enum SASSOp {
HMMA { m: usize, n: usize, k: usize, regs: Vec<u32> }, // Half-precision Matrix Multiply Accumulate
LDG { addr: u64, dest_reg: u32 }, // Load from Global Memory
STG { addr: u64, src_reg: u32 }, // Store to Global Memory
}
// --- [LAYER 3: Tensor Core Microarchitecture] ---
// Models the proprietary hardware: MAC units, pipeline stages, and throughput
struct TensorCoreHardware {
mac_units_per_cycle: usize,
pipeline_depth: usize,
clock_speed_ghz: f64,
registers: HashMap<u32, Vec<f32>>,
}
impl TensorCoreHardware {
fn new(macs: usize, depth: usize, speed: f64) -> Self {
Self {
mac_units_per_cycle: macs,
pipeline_depth: depth,
clock_speed_ghz: speed,
registers: HashMap::new(),
}
}
// Simulate the "Microcode Gap": SASS -> Hardware Signals
fn execute_sass(&mut self, op: SASSOp) -> f64 {
match op {
SASSOp::HMMA { m, n, k, .. } => {
let total_ops = (m * n * k) as f64;
let cycles = (total_ops / self.mac_units_per_cycle as f64).ceil();
let latency = cycles + self.pipeline_depth as f64;
println!("[HW] Executing HMMA {}x{}x{} | Cycles: {:.2} | Latency: {:.2}ns",
m, n, k, cycles, latency / self.clock_speed_ghz);
latency / self.clock_speed_ghz
}
SASSOp::LDG { .. } => {
println!("[HW] Memory Load (L1/L2 Cache Hit)");
20.0 // Fixed 20ns latency for simulation
}
SASSOp::STG { .. } => {
println!("[HW] Memory Store");
10.0
}
}
}
}
// --- [LAYER 4: The Full Stack Orchestrator] ---
struct NvidStack {
hw: TensorCoreHardware,
}
impl NvidStack {
fn run_tensor_op(&mut self, shape: (usize, usize, usize)) {
println!("--- Starting Stack Execution ---");
// 1. PyTorch -> CuTe: Define Layouts
let layout_a = CuTeLayout { shape: vec![shape.0, shape.2], stride: vec![shape.2, 1] };
let layout_b = CuTeLayout { shape: vec![shape.2, shape.1], stride: vec![shape.1, 1] };
println!("[Stack] Layouts Generated: A({:?}), B({:?})", layout_a, layout_b);
// 2. CuTe -> PTX/SASS: Generate Instruction Stream
let program = vec![
SASSOp::LDG { addr: 0x1000, dest_reg: 0 },
SASSOp::LDG { addr: 0x2000, dest_reg: 1 },
SASSOp::HMMA { m: shape.0, n: shape.1, k: shape.2, regs: vec![0, 1, 2] },
SASSOp::STG { addr: 0x3000, src_reg: 2 },
];
// 3. SASS -> Hardware: Execute and measure time
let mut total_time = 0.0;
for inst in program {
total_time += self.hw.execute_sass(inst);
}
println!("--- Stack Execution Complete ---");
println!("Total Wall-Clock Time (Simulated): {:.4} ns", total_time);
}
}
fn main() {
// Initialize hardware simulating a Blackwell-class Tensor Core
// 512 MACs per cycle, 12 stage pipeline, 2.1 GHz
let mut stack = NvidStack {
hw: TensorCoreHardware::new(512, 12, 2.1),
};
// Run a 16x16x16 Matrix Multiply (Typical Tensor Core tile)
stack.run_tensor_op((16, 16, 16));
} |