#import <Foundation/Foundation.h>
#import <Metal/Metal.h>
#import <MetalPerformanceShaders/MetalPerformanceShaders.h>
#include <fcntl.h>
#include <pthread.h>
#include <stdint.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <sys/stat.h>
#include <sys/time.h>
#include <unistd.h>
static double now_sec(void) {
struct timespec ts;
clock_gettime(CLOCK_MONOTONIC, &ts);
return (double)ts.tv_sec + (double)ts.tv_nsec * 1e-9;
}
typedef struct {
off_t offset;
size_t size;
} read_op_t;
typedef struct {
int fd;
const read_op_t *ops;
size_t start_idx;
size_t end_idx;
size_t max_buf;
} worker_arg_t;
static void *pread_worker(void *arg) {
worker_arg_t *w = (worker_arg_t *)arg;
void *buf = NULL;
posix_memalign(&buf, 16384, w->max_buf);
volatile uint8_t sink = 0;
for (size_t i = w->start_idx; i < w->end_idx; i++) {
ssize_t n = pread(w->fd, buf, w->ops[i].size, w->ops[i].offset);
if (n > 0) {
sink ^= ((uint8_t *)buf)[0];
}
}
free(buf);
return (void *)(uintptr_t)sink;
}
static int cmp_double(const void *a, const void *b) {
double da = *(const double *)a;
double db = *(const double *)b;
return (da > db) - (da < db);
}
static int cmp_off(const void *a, const void *b) {
off_t oa = ((const read_op_t *)a)->offset;
off_t ob = ((const read_op_t *)b)->offset;
return (oa > ob) - (oa < ob);
}
static uint64_t rng_state = 0x853c49e6748fea9bULL;
static uint32_t xorshift32(void) {
uint64_t x = rng_state;
x ^= x >> 12;
x ^= x << 25;
x ^= x >> 27;
rng_state = x;
return (uint32_t)((x * 0x2545F4914F6CDD1DULL) >> 32);
}
static void run_op_suite(const char *label, const char *path, int nocache,
read_op_t *ops, size_t n_ops, size_t max_buf,
int n_threads, int reps) {
size_t total_bytes = 0;
for (size_t i = 0; i < n_ops; i++) total_bytes += ops[i].size;
double gbps[16];
for (int r = 0; r < reps; r++) {
int fd = open(path, O_RDONLY);
if (fd < 0) { perror("open"); exit(1); }
if (nocache) {
if (fcntl(fd, F_NOCACHE, 1) < 0) { perror("F_NOCACHE"); exit(1); }
}
pthread_t th[16];
worker_arg_t args[16];
double t0 = now_sec();
for (int t = 0; t < n_threads; t++) {
args[t].fd = fd;
args[t].ops = ops;
args[t].start_idx = (n_ops * t) / n_threads;
args[t].end_idx = (n_ops * (t + 1)) / n_threads;
args[t].max_buf = max_buf;
pthread_create(&th[t], NULL, pread_worker, &args[t]);
}
for (int t = 0; t < n_threads; t++) {
pthread_join(th[t], NULL);
}
double dt = now_sec() - t0;
close(fd);
gbps[r] = ((double)total_bytes / 1e9) / dt;
}
qsort(gbps, reps, sizeof(double), cmp_double);
double med = gbps[reps / 2];
printf("%-46s | th=%d | %6.1f MB | med %6.2f GB/s (min %.2f, max %.2f)\n",
label, n_threads, (double)total_bytes / 1e6, med, gbps[0], gbps[reps - 1]);
fflush(stdout);
}
static void run_metal_bench(void) {
@autoreleasepool {
id<MTLDevice> dev = MTLCreateSystemDefaultDevice();
if (!dev) {
printf("Metal device not available\n");
return;
}
printf("\n=== Metal GPU Benchmark ===\n");
printf("Device: %s | unifiedMemory=%d | recMaxWorkingSet=%.2f GiB\n",
[[dev name] UTF8String], [dev hasUnifiedMemory],
(double)[dev recommendedMaxWorkingSetSize] / (1024.0 * 1024.0 * 1024.0));
id<MTLCommandQueue> q = [dev newCommandQueue];
// Test 1: FP16 GEMM at Flash-Next shapes (K=2560, N=2560 dense and K=2560, N=1920 fused gate+up/expert)
int Ms[] = {512, 1024, 4096};
int Ns[] = {2560, 4096};
int K = 2560;
for (int mi = 0; mi < 3; mi++) {
for (int ni = 0; ni < 2; ni++) {
int M = Ms[mi];
int N = Ns[ni];
size_t bytesA = (size_t)M * K * 2;
size_t bytesB = (size_t)K * N * 2;
size_t bytesC = (size_t)M * N * 2;
id<MTLBuffer> bufA = [dev newBufferWithLength:bytesA options:MTLResourceStorageModeShared];
id<MTLBuffer> bufB = [dev newBufferWithLength:bytesB options:MTLResourceStorageModeShared];
id<MTLBuffer> bufC = [dev newBufferWithLength:bytesC options:MTLResourceStorageModeShared];
memset([bufA contents], 0x3c, bytesA);
memset([bufB contents], 0x3c, bytesB);
MPSMatrixDescriptor *descA = [MPSMatrixDescriptor matrixDescriptorWithRows:M columns:K rowBytes:K*2 dataType:MPSDataTypeFloat16];
MPSMatrixDescriptor *descB = [MPSMatrixDescriptor matrixDescriptorWithRows:K columns:N rowBytes:N*2 dataType:MPSDataTypeFloat16];
MPSMatrixDescriptor *descC = [MPSMatrixDescriptor matrixDescriptorWithRows:M columns:N rowBytes:N*2 dataType:MPSDataTypeFloat16];
MPSMatrix *matA = [[MPSMatrix alloc] initWithBuffer:bufA descriptor:descA];
MPSMatrix *matB = [[MPSMatrix alloc] initWithBuffer:bufB descriptor:descB];
MPSMatrix *matC = [[MPSMatrix alloc] initWithBuffer:bufC descriptor:descC];
MPSMatrixMultiplication *gemm = [[MPSMatrixMultiplication alloc] initWithDevice:dev transposeLeft:NO transposeRight:NO resultRows:M resultColumns:N interiorColumns:K alpha:1.0 beta:0.0];
// Warmup
for (int w = 0; w < 3; w++) {
id<MTLCommandBuffer> cb = [q commandBuffer];
[gemm encodeToCommandBuffer:cb leftMatrix:matA rightMatrix:matB resultMatrix:matC];
[cb commit];
[cb waitUntilCompleted];
}
int iters = 30;
int batch_cmd = 8; // 8 GEMMs per command buffer to measure sustained GPU kernel throughput
double best_tflops = 0.0;
double tflops_arr[5];
for (int r = 0; r < 5; r++) {
double t0 = now_sec();
for (int it = 0; it < iters; it++) {
id<MTLCommandBuffer> cb = [q commandBuffer];
for (int b = 0; b < batch_cmd; b++) {
[gemm encodeToCommandBuffer:cb leftMatrix:matA rightMatrix:matB resultMatrix:matC];
}
[cb commit];
[cb waitUntilCompleted];
}
double dt = now_sec() - t0;
double flops = 2.0 * (double)M * (double)N * (double)K * (double)(iters * batch_cmd);
tflops_arr[r] = (flops / dt) / 1e12;
}
qsort(tflops_arr, 5, sizeof(double), cmp_double);
printf("FP16 GEMM M=%4d K=%4d N=%4d | med %6.2f TFLOP/s (min %.2f, max %.2f)\n",
M, K, N, tflops_arr[2], tflops_arr[0], tflops_arr[4]);
}
}
}
}
int main(int argc, char **argv) {
const char *path = "/tmp/flash_next_ssd_bench.bin";
const size_t file_size = 4ULL * 1024ULL * 1024ULL * 1024ULL; // 4 GiB
printf("=== Phase 0 Hardware Microbench: 13\" MacBook Air M5 ===\n");
printf("Creating 4.0 GiB uncached benchmark file at %s ...\n", path);
fflush(stdout);
int wfd = open(path, O_CREAT | O_TRUNC | O_RDWR, 0600);
if (wfd < 0) { perror("open write"); return 1; }
if (fcntl(wfd, F_NOCACHE, 1) < 0) { perror("F_NOCACHE write"); return 1; }
size_t chunk = 4 * 1024 * 1024;
void *wbuf = NULL;
posix_memalign(&wbuf, 16384, chunk);
for (size_t i = 0; i < chunk / 8; i++) ((uint64_t *)wbuf)[i] = i * 0x9e3779b97f4a7c15ULL;
double tw0 = now_sec();
for (size_t off = 0; off < file_size; off += chunk) {
if (write(wfd, wbuf, chunk) != (ssize_t)chunk) { perror("write"); return 1; }
}
fsync(wfd);
close(wfd);
double tw = now_sec() - tw0;
printf("Wrote 4.0 GiB uncached in %.2f s (%.2f GB/s)\n\n", tw, ((double)file_size / 1e9) / tw);
fflush(stdout);
const int reps = 5;
// 1. Sequential 4 MiB reads (1 GiB per rep, rotating across the 4 GiB file)
{
size_t n_ops = 256; // 1 GiB
read_op_t *ops = malloc(n_ops * sizeof(read_op_t));
for (size_t i = 0; i < n_ops; i++) {
ops[i].offset = (off_t)(i * 4 * 1024 * 1024);
ops[i].size = 4 * 1024 * 1024;
}
run_op_suite("1. Seq 4 MiB reads (F_NOCACHE=1, SSD ceiling)", path, 1, ops, n_ops, 4*1024*1024, 1, reps);
run_op_suite("1b. Seq 4 MiB reads (F_NOCACHE=1, 4 threads)", path, 1, ops, n_ops, 4*1024*1024, 4, reps);
free(ops);
}
// 2. Prefill chunk pattern: 120 missed experts/layer * 16 layers = 1920 experts
// Layout A: 3 reads/expert of 512 KiB (gate, up, down separate tensors, sorted within tensor)
// Layout B: 1 read/expert of 1536 KiB (Strata contiguous .experts copy, sorted within layer)
{
size_t layers = 8; // 8 layers * 120 experts * 1.5 MiB = 1.44 GiB per rep
size_t exps_per_layer = 120;
size_t proj_bytes = 512 * 1024; // 512 KiB (Q2_0 projection size)
size_t exp_bytes = 3 * proj_bytes; // 1.5 MiB
// Layout A: 3 projections per expert (2880 ops)
size_t n_ops_a = layers * exps_per_layer * 3;
read_op_t *ops_a = malloc(n_ops_a * sizeof(read_op_t));
size_t idx = 0;
for (size_t l = 0; l < layers; l++) {
off_t layer_base = (off_t)(l * 512ULL * 1024ULL * 1024ULL); // 512 MiB per layer region
for (size_t p = 0; p < 3; p++) {
off_t tensor_base = layer_base + (off_t)(p * 170ULL * 1024ULL * 1024ULL);
for (size_t e = 0; e < exps_per_layer; e++) {
// Ascending expert IDs (sorted within chunk) with stride ~2-4 experts
size_t exp_id = (e * 2 + (xorshift32() & 1)) % 320;
ops_a[idx].offset = tensor_base + (off_t)(exp_id * proj_bytes);
ops_a[idx].size = proj_bytes;
idx++;
}
}
}
run_op_suite("2a. Prefill 3x512 KiB/exp (std GGUF)", path, 1, ops_a, n_ops_a, proj_bytes, 1, reps);
run_op_suite("2b. Prefill 3x512 KiB/exp (std GGUF)", path, 1, ops_a, n_ops_a, proj_bytes, 4, reps);
run_op_suite("2c. Prefill 3x512 KiB/exp (std GGUF)", path, 1, ops_a, n_ops_a, proj_bytes, 8, reps);
// Layout B: 1 contiguous read of 1.5 MiB per expert (960 ops)
size_t n_ops_b = layers * exps_per_layer;
read_op_t *ops_b = malloc(n_ops_b * sizeof(read_op_t));
idx = 0;
for (size_t l = 0; l < layers; l++) {
off_t layer_base = (off_t)(l * 512ULL * 1024ULL * 1024ULL);
for (size_t e = 0; e < exps_per_layer; e++) {
size_t exp_id = (e * 2 + (xorshift32() & 1)) % 320;
ops_b[idx].offset = layer_base + (off_t)(exp_id * exp_bytes);
ops_b[idx].size = exp_bytes;
idx++;
}
}
run_op_suite("3a. Prefill 1x1.5 MiB/exp (experts' copy)", path, 1, ops_b, n_ops_b, exp_bytes, 1, reps);
run_op_suite("3b. Prefill 1x1.5 MiB/exp (experts' copy)", path, 1, ops_b, n_ops_b, exp_bytes, 4, reps);
run_op_suite("3c. Prefill 1x1.5 MiB/exp (experts' copy)", path, 1, ops_b, n_ops_b, exp_bytes, 8, reps);
free(ops_a);
free(ops_b);
}
// 3. Decode miss pattern: 6 missed experts/layer * 48 layers = 288 experts (~453 MB)
// Random expert IDs within each layer
{
size_t layers = 48;
size_t misses_per_layer = 6;
size_t proj_bytes = 512 * 1024;
size_t exp_bytes = 3 * proj_bytes;
// Decode Layout A: 3 x 512 KiB per missed expert (864 random ops)
size_t n_ops_a = layers * misses_per_layer * 3;
read_op_t *ops_a = malloc(n_ops_a * sizeof(read_op_t));
for (size_t i = 0; i < n_ops_a; i++) {
size_t blk = xorshift32() % (file_size / proj_bytes);
ops_a[i].offset = (off_t)(blk * proj_bytes);
ops_a[i].size = proj_bytes;
}
run_op_suite("4a. Decode 3x512 KiB random (std GGUF)", path, 1, ops_a, n_ops_a, proj_bytes, 1, reps);
run_op_suite("4b. Decode 3x512 KiB random (std GGUF)", path, 1, ops_a, n_ops_a, proj_bytes, 4, reps);
run_op_suite("4c. Decode 3x512 KiB random (std GGUF)", path, 1, ops_a, n_ops_a, proj_bytes, 8, reps);
// Decode Layout B: 1 x 1.5 MiB per missed expert (288 random ops)
size_t n_ops_b = layers * misses_per_layer;
read_op_t *ops_b = malloc(n_ops_b * sizeof(read_op_t));
for (size_t i = 0; i < n_ops_b; i++) {
size_t blk = xorshift32() % (file_size / exp_bytes);
ops_b[i].offset = (off_t)(blk * exp_bytes);
ops_b[i].size = exp_bytes;
}
run_op_suite("5a. Decode 1x1.5 MiB random (experts' copy)", path, 1, ops_b, n_ops_b, exp_bytes, 1, reps);
run_op_suite("5b. Decode 1x1.5 MiB random (experts' copy)", path, 1, ops_b, n_ops_b, exp_bytes, 4, reps);
run_op_suite("5c. Decode 1x1.5 MiB random (experts' copy)", path, 1, ops_b, n_ops_b, exp_bytes, 8, reps);
free(ops_a);
free(ops_b);
}
// 4. PLE n-gram table lookup latency: 16 heads * 320 B row reads per token (512 tokens = 8192 random 4 KiB page reads)
{
size_t n_ops = 4096;
size_t row_page = 4096;
read_op_t *ops = malloc(n_ops * sizeof(read_op_t));
for (size_t i = 0; i < n_ops; i++) {
size_t blk = xorshift32() % (file_size / row_page);
ops[i].offset = (off_t)(blk * row_page);
ops[i].size = row_page;
}
run_op_suite("6a. PLE 4 KiB random reads (1 thread)", path, 1, ops, n_ops, row_page, 1, reps);
run_op_suite("6b. PLE 4 KiB random reads (8 threads)", path, 1, ops, n_ops, row_page, 8, reps);
free(ops);
}
unlink(path);
free(wbuf);
// 5. Metal GPU FP16 GEMM benchmark
run_metal_bench();
return 0;
}