12.3. Automatic Parallelism¶
MXNet automatically constructs computational graphs at the backend. Using a computational graph, the system is aware of all the dependencies, and can selectively execute multiple non-interdependent tasks in parallel to improve speed. For instance, Fig. 12.2.2 in Section 12.2 initializes two variables independently. Consequently the system can choose to execute them in parallel.
Typically, a single operator will use all the computational resources on
all CPUs or on a single GPU. For example, the
dot operator will use
all cores (and threads) on all CPUs, even if there are multiple CPU
processors on a single machine. The same applies to a single GPU. Hence
parallelization is not quite so useful single-device computers. With
multiple devices things matter more. While parallelization is typically
most relevant between multiple GPUs, adding the local CPU will increase
performance slightly. See e.g.,
[Hadjis et al., 2016] for a paper that focuses on
training computer vision models combining a GPU and a CPU. With the
convenience of an automatically parallelizing framework we can
accomplish the same goal in a few lines of Python code. More broadly,
our discussion of automatic parallel computation focuses on parallel
computation using both CPUs and GPUs, as well as the parallelization of
computation and communication. We begin by importing the required
packages and modules. Note that we need at least one GPU to run the
experiments in this section.
from d2l import mxnet as d2l from mxnet import np, npx npx.set_np()
12.3.1. Parallel Computation on CPUs and GPUs¶
Let us start by defining a reference workload to test - the
function below performs 10 matrix-matrix multiplications on the device
of our choosing using data allocated into two variables,
def run(x): return [x.dot(x) for _ in range(10)] x_cpu = np.random.uniform(size=(2000, 2000)) x_gpu = np.random.uniform(size=(6000, 6000), ctx=d2l.try_gpu())
Now we apply the function to the data. To ensure that caching does not play a role in the results we warm up the devices by performing a single pass on each of them prior to measuring.
run(x_cpu) # Warm-up both devices run(x_gpu) npx.waitall() with d2l.benchmark('CPU time: %.4f sec'): run(x_cpu) npx.waitall() with d2l.benchmark('GPU time: %.4f sec'): run(x_gpu) npx.waitall()
CPU time: 0.2851 sec GPU time: 0.3001 sec
If we remove the
waitall() between both tasks the system is free to
parallelize computation on both devices automatically.
with d2l.benchmark('CPU&GPU : %.4f sec'): run(x_cpu) run(x_gpu) npx.waitall()
CPU&GPU : 0.3048 sec
In the above case the total execution time is less than the sum of its parts, since MXNet automatically schedules computation on both CPU and GPU devices without the need for sophisticated code on behalf of the user.
12.3.2. Parallel Computation and Communication¶
In many cases we need to move data between different devices, say between CPU and GPU, or between different GPUs. This occurs e.g., when we want to perform distributed optimization where we need to aggregate the gradients over multiple accelerator cards. Let us simulate this by computing on the GPU and then copying the results back to the CPU.
def copy_to_cpu(x): return [y.copyto(npx.cpu()) for y in x] with d2l.benchmark('Run on GPU: %.4f sec'): y = run(x_gpu) npx.waitall() with d2l.benchmark('Copy to CPU: %.4f sec'): y_cpu = copy_to_cpu(y) npx.waitall()
Run on GPU: 0.3075 sec Copy to CPU: 1.0092 sec
This is somewhat inefficient. Note that we could already start copying
y to the CPU while the remainder of the list is still being
computed. This situatio occurs, e.g., when we compute the (backprop)
gradient on a minibatch. The gradients of some of the parameters will be
available earlier than that of others. Hence it works to our advantage
to start using PCI-Express bus bandwidth while the GPU is still running.
waitall between both parts allows us to simulate this
with d2l.benchmark('Run on GPU and copy to CPU: %.4f sec'): y = run(x_gpu) y_cpu = copy_to_cpu(y) npx.waitall()
Run on GPU and copy to CPU: 1.0582 sec
The total time required for both operations is (as expected)
significantly less than the sum of their parts. Note that this task is
different from parallel computation as it uses a different resource: the
bus between CPU and GPUs. In fact, we could compute on both devices and
communicate, all at the same time. As noted above, there is a dependency
between computation and communication:
y[i] must be computed before
it can be copied to the CPU. Fortunately, the system can copy
y[i] to reduce the total running time.
We conclude with an illustration of the computational graph and its dependencies for a simple two-layer MLP when training on a CPU and two GPUs, as depicted in Fig. 12.3.1. It would be quite painful to schedule the parallel program resulting from this manually. This is where it is advantageous to have a graph based compute backend for optimization.
Modern systems have a variety of devices, such as multiple GPUs and CPUs. They can be used in parallel, asynchronously.
Modern systems also have a variety of resources for communication, such as PCI Express, storage (typically SSD or via network), and network bandwidth. They can be used in parallel for peak efficiency.
The backend can improve performance through through automatic parallel computation and communication.
10 operations were performed in the
runfunction defined in this section. There are no dependencies between them. Design an experiment to see if MXNet will automatically execute them in parallel.
When the workload of an individual operator is sufficiently small, parallelization can help even on a single CPU or GPU. Design an experiment to verify this.
Design an experiment that uses parallel computation on CPU, GPU and communication between both devices.
Use a debugger such as NVIDIA’s Nsight to verify that your code is efficient.
Designing computation tasks that include more complex data dependencies, and run experiments to see if you can obtain the correct results while improving performance.