diff --git a/colossalai/device/__init__.py b/colossalai/device/__init__.py
index e69de29bb..879b60c06 100644
--- a/colossalai/device/__init__.py
+++ b/colossalai/device/__init__.py
@@ -0,0 +1,4 @@
+from .calc_pipeline_strategy import alpa_dp
+from .profile_alpha_beta import profile_alpha_beta
+
+__all__ = ['profile_alpha_beta', 'alpa_dp']
diff --git a/colossalai/device/calc_pipeline_strategy.py b/colossalai/device/calc_pipeline_strategy.py
new file mode 100644
index 000000000..4ab72dfe6
--- /dev/null
+++ b/colossalai/device/calc_pipeline_strategy.py
@@ -0,0 +1,127 @@
+from math import pow
+
+import numpy as np
+
+
+def get_submesh_choices(num_hosts, num_devices_per_host, mode="new"):
+    submesh_choices = []
+    i = 1
+    p = -1
+    while i <= num_devices_per_host:
+        i *= 2
+        p += 1
+    assert pow(2, p) == num_devices_per_host, ("Only supports the cases where num_devices_per_host is power of two, "
+                                               f"while now num_devices_per_host = {num_devices_per_host}")
+    if mode == "alpa":
+        for i in range(p + 1):
+            submesh_choices.append((1, pow(2, i)))
+        for i in range(2, num_hosts + 1):
+            submesh_choices.append((i, num_devices_per_host))
+    elif mode == "new":
+        for i in range(p // 2 + 1):
+            for j in range(i, p - i + 1):
+                submesh_choices.append((pow(2, i), pow(2, j)))
+    return submesh_choices
+
+
+def alpa_dp_impl(num_layers, num_devices, num_microbatches, submesh_choices, compute_cost, max_stage_cost,
+                 best_configs):
+    """Implementation of Alpa DP for pipeline strategy
+	Paper reference: https://www.usenix.org/system/files/osdi22-zheng-lianmin.pdf
+
+	Arguments:
+		num_layers: K
+		num_devices: N*M
+		num_microbatches: B
+		submesh_choices: List[(n_i,m_i)]
+		compute_cost: t_intra
+	"""
+    # For f, layer ID start from 0
+    # f[#pipeline stages, layer id that is currently being considered, number of devices used]
+    f = np.full((num_layers + 1, num_layers + 1, num_devices + 1), np.inf, dtype=np.float32)
+    f_stage_max = np.full((num_layers + 1, num_layers + 1, num_devices + 1), 0.0, dtype=np.float32)
+    f_argmin = np.full((num_layers + 1, num_layers + 1, num_devices + 1, 3), -1, dtype=np.int32)
+    f[0, num_layers, 0] = 0
+    for s in range(1, num_layers + 1):
+        for k in range(num_layers - 1, -1, -1):
+            for d in range(1, num_devices + 1):
+                for m, submesh in enumerate(submesh_choices):
+                    n_submesh_devices = np.prod(np.array(submesh))
+                    if n_submesh_devices <= d:
+                        # TODO: [luzgh]: Why alpa needs max_n_succ_stages? Delete.
+                        # if s - 1 <= max_n_succ_stages[i, k - 1, m, n_config]:
+                        # ...
+                        for i in range(num_layers, k, -1):
+                            stage_cost = compute_cost[k, i, m]
+                            new_cost = f[s - 1, k, d - n_submesh_devices] + stage_cost
+                            if (stage_cost <= max_stage_cost and new_cost < f[s, k, d]):
+                                f[s, k, d] = new_cost
+                                f_stage_max[s, k, d] = max(stage_cost, f_stage_max[s - 1, i, d - n_submesh_devices])
+                                f_argmin[s, k, d] = (i, m, best_configs[k, i, m])
+    best_s = -1
+    best_total_cost = np.inf
+    for s in range(1, num_layers + 1):
+        if f[s, 0, num_devices] < best_total_cost:
+            best_s = s
+            best_total_cost = f[s, 0, num_devices]
+
+    if np.isinf(best_total_cost):
+        return np.inf, None
+
+    total_cost = f[best_s, 0, num_devices] + (num_microbatches - 1) * f_stage_max[best_s, 0, num_devices]
+    current_s = best_s
+    current_layer = 0
+    current_devices = num_devices
+
+    res = []
+    while current_s > 0 and current_layer < num_layers and current_devices > 0:
+        next_start_layer, submesh_choice, autosharding_choice = (f_argmin[current_s, current_layer, current_devices])
+        assert next_start_layer != -1 and current_devices != -1
+        res.append(((current_layer, next_start_layer), submesh_choice, autosharding_choice))
+        current_s -= 1
+        current_layer = next_start_layer
+        current_devices -= np.prod(np.array(submesh_choices[submesh_choice]))
+    assert (current_s == 0 and current_layer == num_layers and current_devices == 0)
+
+    return total_cost, res
+
+
+def alpa_dp(num_layers,
+            num_devices,
+            num_microbatches,
+            submesh_choices,
+            num_autosharding_configs,
+            compute_cost,
+            gap=1e-6):
+    """Alpa auto stage dynamic programming.
+	Code reference: https://github.com/alpa-projects/alpa/blob/main/alpa/pipeline_parallel/stage_construction.py
+
+    Arguments:
+        submesh_choices: List[(int,int)]
+        num_autosharding_configs: Max number of t_intra(start_layer, end_layer, LogicalMesh)
+        compute_cost: np.array(num_layers,num_layers,num_submesh_choices,num_autosharding_configs)
+    """
+    assert np.shape(compute_cost) == (num_layers, num_layers, len(submesh_choices),
+                                      num_autosharding_configs), "Cost shape wrong."
+    all_possible_stage_costs = np.sort(np.unique(compute_cost))
+    best_cost = np.inf
+    best_solution = None
+    last_max_stage_cost = 0.0
+    # TODO: [luzgh]: Why alpa needs the num_autosharding_configs dimension in compute_cost?
+    # In dp_impl it seems the argmin n_config will be chosen. Just amin here.
+    best_configs = np.argmin(compute_cost, axis=3)
+    best_compute_cost = np.amin(compute_cost, axis=3)
+    assert len(all_possible_stage_costs), "no solution in auto stage construction."
+    for max_stage_cost in all_possible_stage_costs:
+        if max_stage_cost * num_microbatches >= best_cost:
+            break
+        if max_stage_cost - last_max_stage_cost < gap:
+            continue
+        cost, solution = alpa_dp_impl(num_layers, num_devices, num_microbatches, submesh_choices, best_compute_cost,
+                                      max_stage_cost, best_configs)
+        if cost < best_cost:
+            best_cost = cost
+            best_solution = solution
+        last_max_stage_cost = max_stage_cost
+
+    return best_cost, best_solution
diff --git a/colossalai/device/profile_alpha_beta.py b/colossalai/device/profile_alpha_beta.py
new file mode 100644
index 000000000..2d053ddbe
--- /dev/null
+++ b/colossalai/device/profile_alpha_beta.py
@@ -0,0 +1,120 @@
+import fcntl
+import math
+import os
+import time
+
+import torch
+import torch.distributed as dist
+import torch.multiprocessing as mp
+
+MB = int((1 << 10) * 1e3)
+GB = int((1 << 20) * 1e3)
+Byte = 4
+FRAMEWORK = 0
+NON_SENSE = (0.1, 0.1)
+
+
+def printflock(*msgs):
+    """ solves multi-process interleaved print problem """
+    with open(__file__, "r") as fh:
+        fcntl.flock(fh, fcntl.LOCK_EX)
+        try:
+            print(*msgs)
+        finally:
+            fcntl.flock(fh, fcntl.LOCK_UN)
+
+
+def profile(device1d, nbytes, ctype):
+    warmup = 5
+    repeat = 25
+    rank = dist.get_rank()
+    src_device_num = device1d[0]
+    wsize = len(device1d)
+    group = dist.new_group(device1d)
+
+    torch.cuda.set_device(rank)
+    device = torch.device("cuda", rank)
+    buf = torch.randn(nbytes // 4).to(device)
+
+    torch.cuda.synchronize()
+    # warmup
+    for _ in range(warmup):
+        if ctype == "a":
+            dist.all_reduce(buf, op=dist.ReduceOp.SUM, group=group)
+        elif ctype == "b":
+            dist.broadcast(buf, src=src_device_num, group=group)
+    torch.cuda.synchronize()
+
+    dist.barrier()
+    begin = time.perf_counter()
+    for _ in range(repeat):
+        if ctype == "a":
+            dist.all_reduce(buf, op=dist.ReduceOp.SUM, group=group)
+        elif ctype == "b":
+            dist.broadcast(buf, src=src_device_num, group=group)
+    torch.cuda.synchronize()
+    end = time.perf_counter()
+    dist.barrier()
+
+    if rank == src_device_num:
+        avg_time_s = (end - begin) / repeat - FRAMEWORK
+        alg_band = nbytes / avg_time_s
+        if ctype == "b":
+            bus_band = alg_band
+        elif ctype == "a":
+            bus_band = 2 * (wsize - 1) / wsize * alg_band
+        print(
+            f"GPU:{rank}, Bytes: {nbytes} B,Time: {round(avg_time_s * 1e6,2)} us, Bus bandwidth: {round(bus_band / GB,2)} GB/s"
+        )
+        return (avg_time_s, alg_band)
+    else:
+        return NON_SENSE    # Just a placeholder
+
+
+def profile_latency(device1d, it=3, ctype="a"):
+    latency = []
+    for i in range(it):
+        nbytes = int(Byte << i)
+        (t, _) = profile(device1d, nbytes, ctype)
+        latency.append(t)
+    return min(latency)
+
+
+def profile_bandwidth(device1d, maxbytes, ctype="a"):
+    (_, bandwidth) = profile(device1d, maxbytes, ctype)
+    return bandwidth
+
+
+def profile_ab(rank, *args):
+    wsize = int(torch.cuda.device_count())
+    device1d = args[0]
+    return_dict = args[1]
+    ctype = args[2]
+    os.environ['MASTER_ADDR'] = 'localhost'
+    os.environ['MASTER_PORT'] = '29020'
+    dist.init_process_group(backend=dist.Backend.NCCL, init_method='env://', world_size=wsize, rank=rank)
+
+    device = torch.device("cuda", rank)
+    max_nbytes = torch.tensor(torch.cuda.mem_get_info(device)[0]).to(device)
+    max_nbytes = min(int(4 * GB), int(GB << int(math.log2(max_nbytes.item() / GB))))
+
+    if rank == device1d[0]:
+        print(f"max_nbytes: {max_nbytes} B")
+
+    alpha = profile_latency(device1d, it=5, ctype=ctype)
+    beta = 1 / profile_bandwidth(device1d, maxbytes=max_nbytes, ctype=ctype)
+
+    if rank == device1d[0]:
+        print(f"alpha(us): {round(alpha * 1e6,2)}, beta(us/GB): {round(beta * 1e6 * GB,2)}")
+    return_dict[rank] = (alpha, beta)
+
+
+def profile_alpha_beta(device1d):
+    assert torch.cuda.is_available()
+    assert len(device1d) > 0 and len(device1d) <= int(torch.cuda.device_count())
+
+    manager = mp.Manager()
+    return_dict = manager.dict()
+    ctype = "a"
+    mp.spawn(profile_ab, args=[device1d, return_dict, ctype], nprocs=int(torch.cuda.device_count()))
+    return return_dict[device1d[0]]
diff --git a/tests/test_device/test_alpha_beta.py b/tests/test_device/test_alpha_beta.py
new file mode 100644
index 000000000..5b076fdf0
--- /dev/null
+++ b/tests/test_device/test_alpha_beta.py
@@ -0,0 +1,14 @@
+import pytest
+
+from colossalai.device import profile_alpha_beta
+
+
+@pytest.mark.skip(reason="Skip because assertion fails for CI devices")
+def test_profile_alpha_beta():
+    physical_devices = [0, 1, 2, 3]
+    (alpha, beta) = profile_alpha_beta(physical_devices)
+    assert alpha > 0 and alpha < 1e-4 and beta > 0 and beta < 1e-10
+
+
+if __name__ == '__main__':
+    test_profile_alpha_beta()