// ❌ BAD: Using undefined
pub fn badInit() Actor {
var actor: Actor = undefined; // NEVER do this
actor.id = generateId();
// What about other fields? Undefined behavior!
return actor;
}
// ✅ GOOD: Explicit initialization
pub fn goodInit() Actor {
return Actor{
.id = generateId(),
.state = .initializing,
.mailbox = BoundedQueue.init(),
.reductions = INITIAL_REDUCTIONS,
.supervisor = null,
.children = BoundedArray.init(),
// Every field explicitly initialized
};
}
pub const Limits = struct {
// Tiger Style: All limits are fixed at compile time
pub const MAX_ACTORS = 1_000_000;
pub const MAX_MAILBOX_SIZE = 256;
pub const MAX_MESSAGE_SIZE = 64 * 1024; // 64KB
pub const MAX_CHILDREN = 100;
pub const MAX_REDUCTIONS = 2000;
pub const MAX_RETRIES = 10;
pub const MAX_DEVICES = 16;
pub const ACTOR_HEAP_SIZE = 1024 * 1024; // 1MB
pub const ACTOR_STACK_SIZE = 64 * 1024; // 64KB
};
// All data structures bounded
pub const BoundedQueue = struct(comptime T: type, comptime max: usize) {
items: [max]T,
head: usize = 0,
tail: usize = 0,
count: usize = 0,
pub fn push(self: *@This(), item: T) !void {
if (self.count >= max) {
return error.QueueFull;
}
self.items[self.tail] = item;
self.tail = (self.tail + 1) % max;
self.count += 1;
}
pub fn pop(self: *@This()) ?T {
if (self.count == 0) return null;
const item = self.items[self.head];
self.head = (self.head + 1) % max;
self.count -= 1;
return item;
}
};
pub const ActorSystem = struct {
actors: []Actor,
worker_count: u32,
pub fn spawn(self: *ActorSystem, behavior: Behavior) !ActorId {
// Precondition assertions
assert(self.worker_count > 0);
assert(self.actors.len < Limits.MAX_ACTORS);
const actor = try self.allocateActor();
// Invariant: actor must be in valid state
assert(actor.state == .initializing or actor.state == .running);
// Postcondition: actor is registered
defer assert(self.registry.contains(actor.id));
return actor.id;
}
// Pair assertions for critical data
pub fn transferMessage(self: *ActorSystem, msg: Message) !void {
const sender_before = self.getActor(msg.from).mailbox.count;
const receiver_before = self.getActor(msg.to).mailbox.count;
try self.doTransfer(msg);
const sender_after = self.getActor(msg.from).mailbox.count;
const receiver_after = self.getActor(msg.to).mailbox.count;
// Pair assertion: one message moved
assert(sender_after == sender_before - 1);
assert(receiver_after == receiver_before + 1);
}
};
// All errors are explicit
pub const GpuError = error{
OutOfMemory,
DeviceLost,
KernelTimeout,
InvalidConfiguration,
UnsupportedOperation,
};
pub fn launchKernel(kernel: Kernel, device: Device) GpuError!void {
// Check preconditions
if (!device.isHealthy()) {
return error.DeviceLost;
}
if (kernel.memory_requirement > device.available_memory) {
return error.OutOfMemory;
}
// Fail fast on programmer errors
assert(kernel.grid.x > 0); // This should never be 0
assert(kernel.grid.y > 0);
assert(kernel.grid.z > 0);
// Handle all possible failures
const result = device.backend.launch(kernel) catch |err| {
switch (err) {
error.CudaError => return error.DeviceLost,
error.Timeout => return error.KernelTimeout,
else => return err,
}
};
// Verify success
assert(result.status == .success);
}
// Document performance assumptions
pub const PerformanceModel = struct {
// Memory bandwidth: 900 GB/s (A100)
// L2 cache: 40 MB
// SM count: 108
// Registers per SM: 65536
pub fn estimateMatmulTime(m: usize, n: usize, k: usize) f64 {
// FLOPs = 2 * M * N * K
const flops = 2 * m * n * k;
// Memory transfers = M*K + K*N + M*N elements
const memory_bytes = @sizeOf(f32) * (m * k + k * n + m * n);
// Assuming peak performance
const compute_time = @as(f64, flops) / (19.5 * 1e12); // 19.5 TFLOPS
const memory_time = @as(f64, memory_bytes) / (900 * 1e9); // 900 GB/s
// Actual time is max of compute and memory bound
return @max(compute_time, memory_time);
}
pub fn canFitInSharedMemory(tensor_size: usize) bool {
const shared_mem_per_sm = 164 * 1024; // 164 KB on A100
return tensor_size <= shared_mem_per_sm;
}
};
pub const BatchProcessor = struct {
// Amortize expensive operations
pub fn processBatch(
messages: []const Message,
actor: *Actor,
) !void {
// Sort messages by type for better branch prediction
var sorted = try allocator.alloc(Message, messages.len);
defer allocator.free(sorted);
std.sort.sort(Message, sorted, {}, struct {
fn lessThan(ctx: void, a: Message, b: Message) bool {
_ = ctx;
return @intFromEnum(a.type) < @intFromEnum(b.type);
}
}.lessThan);
// Process in batches by type
var i: usize = 0;
while (i < sorted.len) {
const msg_type = sorted[i].type;
var j = i + 1;
// Find end of same-type messages
while (j < sorted.len and sorted[j].type == msg_type) : (j += 1) {}
// Process batch
try processSameType(sorted[i..j], actor);
i = j;
}
}
// Batch GPU operations
pub fn launchKernelBatch(
kernels: []const Kernel,
device: Device,
) !void {
// Use CUDA streams for concurrent execution
var streams: [MAX_STREAMS]Stream = undefined;
for (0..@min(kernels.len, MAX_STREAMS)) |i| {
streams[i] = try device.createStream();
}
defer for (streams) |s| device.destroyStream(s);
// Launch all kernels asynchronously
for (kernels, 0..) |kernel, i| {
const stream = streams[i % MAX_STREAMS];
try device.launchAsync(kernel, stream);
}
// Wait for all to complete
for (streams[0..@min(kernels.len, MAX_STREAMS)]) |stream| {
try stream.synchronize();
}
}
};
pub const MemoryEfficientTensor = struct {
// Use minimum precision needed
data: union(enum) {
f32: []f32,
f16: []f16,
bf16: []bf16,
i8: []i8, // Quantized
i4: []u8, // 4-bit packed
},
shape: TensorShape,
// Lazy allocation
allocated: bool = false,
pub fn allocate(self: *@This()) !void {
if (self.allocated) return;
const num_elements = self.shape.numElements();
switch (self.data) {
.f32 => |*d| d.* = try allocator.alloc(f32, num_elements),
.f16 => |*d| d.* = try allocator.alloc(f16, num_elements),
.bf16 => |*d| d.* = try allocator.alloc(bf16, num_elements),
.i8 => |*d| d.* = try allocator.alloc(i8, num_elements),
.i4 => |*d| d.* = try allocator.alloc(u8, (num_elements + 1) / 2),
}
self.allocated = true;
}
// Memory pooling
pub fn deallocate(self: *@This(), pool: *TensorPool) !void {
if (!self.allocated) return;
// Return to pool instead of freeing
try pool.return(self);
self.allocated = false;
}
};
pub const CacheOptimized = struct {
// Structure padding for cache alignment
pub const CacheAlignedActor = struct {
// Hot data on same cache line
hot: struct {
id: ActorId,
state: State,
reductions: i32,
message_count: u32,
padding: [40]u8, // Pad to 64 bytes
} align(64),
// Cold data on separate cache lines
cold: struct {
supervisor: ?ActorId,
children: BoundedArray(ActorId, MAX_CHILDREN),
metadata: Metadata,
} align(64),
};
// Prefetching
pub fn processActors(actors: []Actor) !void {
// Prefetch next actors while processing current
for (actors, 0..) |*actor, i| {
// Prefetch next few actors
if (i + 1 < actors.len) {
@prefetch(&actors[i + 1], .{
.rw = .read,
.locality = 1,
.cache = .data,
});
}
// Process current actor
try actor.process();
}
}
};
pub const CpuOptimized = struct {
// Comptime generation of optimized code paths
pub fn matmul(comptime features: CpuFeatures) type {
return struct {
pub fn compute(a: []f32, b: []f32, c: []f32, m: usize, n: usize, k: usize) void {
if (comptime features.avx512) {
matmulAVX512(a, b, c, m, n, k);
} else if (comptime features.avx2) {
matmulAVX2(a, b, c, m, n, k);
} else if (comptime features.sse4) {
matmulSSE4(a, b, c, m, n, k);
} else {
matmulScalar(a, b, c, m, n, k);
}
}
};
}
// Runtime dispatch to comptime-optimized versions
pub fn createOptimizedMatmul() MatmulFn {
const cpu = detectCpuFeatures();
if (cpu.avx512) {
return matmul(.{ .avx512 = true }).compute;
} else if (cpu.avx2) {
return matmul(.{ .avx2 = true }).compute;
} else if (cpu.sse4) {
return matmul(.{ .sse4 = true }).compute;
} else {
return matmul(.{}).compute;
}
}
};
pub fn generateOptimizedKernel(comptime spec: KernelSpec) type {
return struct {
// Generate different versions for different architectures
pub const cuda_sm70 = if (spec.targets.cuda_sm70)
generateCudaKernel(spec, .sm_70)
else
null;
pub const cuda_sm80 = if (spec.targets.cuda_sm80)
generateCudaKernel(spec, .sm_80)
else
null;
pub const cuda_sm90 = if (spec.targets.cuda_sm90)
generateCudaKernel(spec, .sm_90)
else
null;
// Runtime dispatch
pub fn launch(device: Device, args: anytype) !void {
switch (device.compute_capability) {
90 => try launchKernel(cuda_sm90.?, device, args),
80...89 => try launchKernel(cuda_sm80.?, device, args),
70...79 => try launchKernel(cuda_sm70.?, device, args),
else => return error.UnsupportedDevice,
}
}
};
}
test "bounded operations never overflow" {
var queue = BoundedQueue(u32, 10).init();
// Fill queue
for (0..10) |i| {
try queue.push(@intCast(i));
}
// Should fail on overflow
const result = queue.push(10);
try testing.expectError(error.QueueFull, result);
// Should handle empty queue
for (0..10) |_| {
_ = queue.pop();
}
try testing.expect(queue.pop() == null);
}
test "assertions catch invariant violations" {
if (builtin.mode != .Debug) return; // Assertions only in debug
var system = try ActorSystem.init(.{
.worker_count = 0, // Invalid!
});
// Should panic in debug mode
const result = std.debug.panic_test(
system.spawn,
.{testBehavior},
);
try testing.expect(result == .panic);
}
test "memory limits are enforced" {
var actor = try Actor.init();
// Try to allocate more than allowed
const huge_size = Limits.ACTOR_HEAP_SIZE + 1;
const result = actor.heap.alloc(u8, huge_size);
try testing.expectError(error.OutOfMemory, result);
}
test "performance meets requirements" {
const start = std.time.nanoTimestamp();
// Message passing latency
{
var system = try ActorSystem.init(.{});
const actor = try system.spawn(echoActor, .{});
const msg_start = std.time.nanoTimestamp();
try actor.send(.{ .data = "test" });
_ = try actor.receive(100);
const msg_elapsed = std.time.nanoTimestamp() - msg_start;
// Should be under 1 microsecond
try testing.expect(msg_elapsed < 1000);
}
// GPU kernel launch overhead
{
const device = try selectDevice();
const kernel = simpleKernel;
const launch_start = std.time.nanoTimestamp();
try kernel.launch(device, .{});
const launch_elapsed = std.time.nanoTimestamp() - launch_start;
// Should be under 10 microseconds
try testing.expect(launch_elapsed < 10_000);
}
const total_elapsed = std.time.nanoTimestamp() - start;
std.debug.print("Performance test completed in {}ns\n", .{total_elapsed});
}