GEMV 6: Routes and Fabric DSDs, Part I

GEMV 6: Routes and Fabric DSDs, Part I

Continuing from the previous example, we now break up a single GEMV computation among two PEs.

The host program copies b into the y tensor of the left PE. The left PE also gets the first N/2 columns of A and the first N/2 values of x, and the right PE gets the last N/2 columns of A and last N/2 values of x.

The left and right PE both increment their local y tensors by computing their piece of Ax. Then, the left PE sends its result to the right PE, which increments its y tensor by the received values.

Last, the host copies y from the right PE, and checks that the result is correct.

To send data from the left PE to the right PE, we must specify a route, known as a color. In layout.csl, @set_color_config specifies that on the left PE, color 0 will receive data, or wavelets, from the compute element (CE) up the RAMP, and transmit them to the EAST. On the right PE, color 0 will receive wavelets form the WEST, and then transmit them down the RAMP to the CE. @set_tile_code passes the ID of this color to pe_program as a parameter named send_color, and also sets a paremeter called pe_id, to diffentiate if the program is running on the left or the right PE.

The send_right function executed on the left PE defines a fabout_dsd called out_dsd that sends M wavelets along the color route specified by send_color. out_dsd is used as the destination operand of @fmovs, and y_dsd as the source operand. Thus, this operation sends the M elements accessed by y_dsd along the fabric as specified by out_dsd.

The recv_left function executed on the right PE receives the data in a fabin_dsd named in_dsd, used in an @fadds operation that increments the M elements of y on this PE by the M received values.

Note that this program also provides an example of a local task. The @fmovs and @fadds operations are performed asynchronously; when these operations are done, the color exit_color is activated, which activates the task exit_task. This task unblocks memcpy’s command stream, allowing additional commands from the host program to proceed.


See GEMV Tutorial 6: Routes and Fabric DSDs for a step-by-step walkthrough of this example.


// matrix dimensions on each PE
param M: i16;
param N: i16;

// Colors
const send_color: color = @get_color(0); // Color used to send/recv data between PEs

// This example only uses 2 PEs
const memcpy = @import_module("<memcpy/get_params>", .{
  .width = 2,
  .height = 1,

layout {
  // PE coordinates are (column, row)
  @set_rectangle(2, 1);

  // Left PE (0, 0)
  @set_tile_code(0, 0, "pe_program.csl", .{
    .memcpy_params = memcpy.get_params(0),
    .M = M,
    .N_per_PE = N / 2,
    .pe_id = 0,
    .send_color = send_color

  // Left PE sends its result to the right
  @set_color_config(0, 0, send_color, .{.routes = .{ .rx = .{RAMP}, .tx = .{EAST} }});

  // Right PE (1, 0)
  @set_tile_code(1, 0, "pe_program.csl", .{
    .memcpy_params = memcpy.get_params(1),
    .M = M,
    .N_per_PE = N / 2,
    .pe_id = 1,
    .send_color = send_color

  // Right PE receives result of left PE
  @set_color_config(1, 0, send_color, .{.routes = .{ .rx = .{WEST}, .tx = .{RAMP} }});

  // export symbol names
  @export_name("A", [*]f32, true);
  @export_name("x", [*]f32, true);
  @export_name("y", [*]f32, true);
  @export_name("compute", fn()void);


param memcpy_params: comptime_struct;

// Matrix dimensions
param M: i16;
param N_per_PE: i16;

// ID of PE (0 is left, 1 is right)
param pe_id: i16;

// Colors
param send_color: color; // Color used to send/recv data between PEs

// Queue IDs
const send_color_oq = @get_output_queue(2);
const send_color_iq = @get_input_queue(2);

// Task ID used by a local task to unblock cmd stream
const exit_task_id: local_task_id = @get_local_task_id(9);

// memcpy module provides infrastructure for copying data
// and launching functions from the host
const sys_mod = @import_module("<memcpy/memcpy>", memcpy_params);

// 48 kB of global memory contain A, x, b, y
var A: [M*N_per_PE]f32; // A is stored column major
var x: [N_per_PE]f32;
var y: [M]f32;

// DSDs for accessing A, b, y
// A_dsd accesses column of A
var A_dsd = @get_dsd(mem1d_dsd, .{ .tensor_access = |i|{M} -> A[i] });
var y_dsd = @get_dsd(mem1d_dsd, .{ .tensor_access = |i|{M} -> y[i] });

// ptrs to A, x, b, y will be advertised as symbols to host
var A_ptr: [*]f32 = &A;
var x_ptr: [*]f32 = &x;
var y_ptr: [*]f32 = &y;

// Compute gemv
fn gemv() void {
  // Loop over all columns of A
  for (@range(i16, N_per_PE)) |i| {
    // Calculate contribution to A*x from ith column of A, ith elem of x
    @fmacs(y_dsd, y_dsd, A_dsd, x[i]);
    // Move A_dsd to next column of A
    A_dsd = @increment_dsd_offset(A_dsd, M, f32);

fn send_right() void {
  const out_dsd = @get_dsd(fabout_dsd, .{
                    .fabric_color = send_color, .extent = M,
                    .output_queue = send_color_oq
  // After fmovs is done, activate exit_task to unblock cmd_stream
  @fmovs(out_dsd, y_dsd, .{ .async = true, .activate = exit_task_id });

fn recv_left() void {
  const in_dsd = @get_dsd(fabin_dsd, .{
                   .fabric_color = send_color, .extent = M,
                   .input_queue = send_color_iq
  // After fadds is done, activate exit_task to unblock cmd stream
  @fadds(y_dsd, y_dsd, in_dsd, .{ .async = true, .activate = exit_task_id });

// Call gemv function and send/ receive partial result y
fn compute() void {
  if (pe_id == 0) {
  } else {

task exit_task() void {

comptime {
  // When exit_task_id is activated, exit_task will execute
  @bind_local_task(exit_task, exit_task_id);

  // On WSE-3, we must explicitly initialize input and output queues
  if (@is_arch("wse3")) {
    @initialize_queue(send_color_oq, .{ .color = send_color });
    @initialize_queue(send_color_iq, .{ .color = send_color });

  @export_symbol(A_ptr, "A");
  @export_symbol(x_ptr, "x");
  @export_symbol(y_ptr, "y");

#!/usr/bin/env cs_python

import argparse
import json
import numpy as np

from cerebras.sdk.runtime.sdkruntimepybind import SdkRuntime, MemcpyDataType, MemcpyOrder # pylint: disable=no-name-in-module

# Read arguments
parser = argparse.ArgumentParser()
parser.add_argument('--name', help="the test compile output dir")
parser.add_argument('--cmaddr', help="IP:port for CS system")
args = parser.parse_args()

# Get matrix dimensions from compile metadata
with open(f"{}/out.json", encoding='utf-8') as json_file:
  compile_data = json.load(json_file)

# Matrix dimensions
N = int(compile_data['params']['N'])
M = int(compile_data['params']['M'])

# Construct A, x, b
A = np.arange(M*N, dtype=np.float32).reshape(M,N)
x = np.full(shape=N, fill_value=1.0, dtype=np.float32)
b = np.full(shape=M, fill_value=2.0, dtype=np.float32)

# Calculate expected y
y_expected = A@x + b

# Size of N dimension on each PE
N_per_PE = N // 2

# Construct a runner using SdkRuntime
runner = SdkRuntime(, cmaddr=args.cmaddr)

# Get symbols for A, x, y on device
A_symbol = runner.get_id('A')
x_symbol = runner.get_id('x')
y_symbol = runner.get_id('y')

# Load and run the program

# Copy b into y of PE (0, 0)
runner.memcpy_h2d(y_symbol, b, 0, 0, 1, 1, M, streaming=False,
  order=MemcpyOrder.ROW_MAJOR, data_type=MemcpyDataType.MEMCPY_32BIT, nonblock=False)

# Copy A in column major format
# PE (0, 0) gets first N/2 columns; PE (1, 0) gets last N/2 columns
runner.memcpy_h2d(A_symbol, A.transpose().ravel(), 0, 0, 2, 1, M*N_per_PE, streaming=False,
  order=MemcpyOrder.ROW_MAJOR, data_type=MemcpyDataType.MEMCPY_32BIT, nonblock=False)

# PE (0, 0) gets first N/2 elements; PE (1, 0) gets last N/2 elements
runner.memcpy_h2d(x_symbol, x, 0, 0, 2, 1, N_per_PE, streaming=False,
  order=MemcpyOrder.ROW_MAJOR, data_type=MemcpyDataType.MEMCPY_32BIT, nonblock=False)

# Launch the compute function on device
runner.launch('compute', nonblock=False)

# Copy y back from PE (1, 0)
y_result = np.zeros([M], dtype=np.float32)
runner.memcpy_d2h(y_result, y_symbol, 1, 0, 1, 1, M, streaming=False,
  order=MemcpyOrder.ROW_MAJOR, data_type=MemcpyDataType.MEMCPY_32BIT, nonblock=False)

# Stop the program

# Ensure that the result matches our expectation
np.testing.assert_allclose(y_result, y_expected, atol=0.01, rtol=0)

#!/usr/bin/env bash

set -e

cslc --arch=wse2 ./layout.csl --fabric-dims=11,3 \
--fabric-offsets=4,1 --params=M:4,N:6 -o out --memcpy --channels 1
cs_python --name out