[llvm-dev] RFC: Proposing an LLVM subproject for parallelism runtime and support libraries
Hal Finkel via llvm-dev
llvm-dev at lists.llvm.org
Wed Mar 9 16:42:01 PST 2016
----- Original Message -----
> From: "Jason Henline" <jhen at google.com>
> To: "Hal Finkel" <hfinkel at anl.gov>
> Cc: llvm-dev at lists.llvm.org
> Sent: Wednesday, March 9, 2016 5:04:53 PM
> Subject: Re: [llvm-dev] RFC: Proposing an LLVM subproject for
> parallelism runtime and support libraries
> Hi Hal,
> Thanks for taking a look at the proposal.
> The current version of StreamExecutor has partial support for a
> "host" platform which performs work on the CPU. It's interface is
> the same as the that of the CUDA platform discussed in the design
> documentation, but right now it does not support launching
> user-defined kernels, so it is very limited. The host platform does
> manage a thread pool internally and uses those threads to execute
> the "canned" StreamExecutor operations (BLAS, FFT, etc.), but that's
> all it can currently do. I think it would be relatively easy to
> extend the host platform to support launching of user-defined
> kernels, and then its functionality may overlap quite a bit with
> OpenMP, but we don't have any active plans to add that support at
> this time.
I think that having support for running host-side tasks makes this library much more useful, not only for acceleratorless systems, but even for those with accelerators (especially if you can place dependency edges between the host tasks and the accelerator tasks).
Also, does your implementation support, or do you plan on supporting, CUDA-style unified memory between host and device?
Thanks again,
Hal
> However, this is something we may pursue in the long run because of
> the added flexibility it would provide for porting accelerator code
> to a device without an accelerator, etc.
> On Wed, Mar 9, 2016 at 2:31 PM Hal Finkel < hfinkel at anl.gov > wrote:
> > > From: "Jason Henline via llvm-dev" < llvm-dev at lists.llvm.org >
> >
>
> > > To: llvm-dev at lists.llvm.org
> >
>
> > > Sent: Wednesday, March 9, 2016 4:20:15 PM
> >
>
> > > Subject: [llvm-dev] RFC: Proposing an LLVM subproject for
> > > parallelism
> > > runtime and support libraries
> >
>
> > > At Google we're doing a lot of work on parallel programming
> > > models
> > > for CPUs, GPUs and other platforms. One place where we're
> > > investing
> > > a lot are parallel libraries, especially those closely tied to
> > > compiler technology like runtime and math libraries. We would
> > > like
> > > to develop these in the open, and the natural place seems to be
> > > as
> > > a
> > > subproject in LLVM if others in the community are interested.
> >
>
> > > Initially, we'd like to open source our StreamExecutor runtime
> > > library, which is used for simplifying the management of
> > > data-parallel workflows on accelerator devices and can also be
> > > extended to support other hardware platforms. We'd like to teach
> > > Clang to use StreamExecutor when targeting CUDA and work on other
> > > integrations, but that makes much more sense if it is part of the
> > > LLVM project.
> >
>
> > > However, we think the LLVM subproject should be organized as a
> > > set
> > > of
> > > several libraries with StreamExecutor as just the first instance.
> > > As
> > > just one example of how creating a unified parallelism subproject
> > > could help with code sharing, the StreamExecutor library contains
> > > some nice wrappers around the CUDA driver API and OpenCL API that
> > > create a unified API for managing all kinds of GPU devices. This
> > > unified GPU wrapper would be broadly applicable for libraries
> > > that
> > > need to communicate with GPU devices.
> >
>
> > > Of course, there is already an LLVM subproject for a parallel
> > > runtime
> > > library: OpenMP! So there is a question of how it would fit into
> > > this picture. Eventually, it might make sense to pull in the
> > > OpenMP
> > > project as a library in this proposed new subproject. In
> > > particular,
> > > there is a good chance that OpenMP and StreamExecutor could share
> > > code for offloading to GPUs and managing workloads on those
> > > devices.
> > > This is discussed at the end of the StreamExecutor documentation
> > > below. However, if it turns out that the needs of OpenMP are too
> > > specialized to fit well in a generic parallelism project, then it
> > > may make sense to leave OpenMP as a separate LLVM subproject so
> > > it
> > > can focus on serving the particular needs of OpenMP.
> >
>
> > The document starts by talking about work you're doing on "CPUs,
> > GPUs
> > and other platforms", but you've only really discussed accelerators
> > here. I'm wondering if there is any overlap, either current or
> > planned, with the functionality that host-side OpenMP provides. For
> > example, is there some kind of host-side thread pool / task queue?
>
> > Thanks in advance,
>
> > Hal
>
> > P.S. I'm really happy that it looks like you have a sane API here
> > for
> > handling multi-GPU systems. Dealing with cudaSetDevice is a real
> > pain.
>
> > > Documentation for the StreamExecutor library that is being
> > > proposed
> > > for open-sourcing is included below to give a sense of what it
> > > is,
> > > in order to give context for how it might fit into a general
> > > parallelism LLVM subproject.
> >
>
> > > What do folks think? Is there general interest in something like
> > > this? If so, we can start working on getting a project in place
> > > and
> > > sketching out a skeleton for how it would be organized, as well
> > > as
> > > contributing StreamExecutor to it. We're happy to iterate on the
> > > particulars to figure out what works for the community.
> >
>
> > > =============================================
> >
>
> > > StreamExecutor Runtime Library Documentation
> >
>
> > > =============================================
> >
>
> > > What is StreamExecutor?
> >
>
> > > ========================
> >
>
> > > **StreamExecutor** is a unified wrapper around the **CUDA** and
> > > **OpenCL** host-side programming models (runtimes). It lets host
> > > code target either CUDA or OpenCL devices with
> > > identically-functioning data-parallel kernels. StreamExecutor
> > > manages the execution of concurrent work targeting the
> > > accelerator
> > > similarly to how an Executor_ from the Google APIs client library
> > > manages the execution of concurrent work on the host.
> >
>
> > > .. _Executor:
> > > http://google.github.io/google-api-cpp-client/latest/doxygen/classgoogleapis_1_1thread_1_1Executor.html
> >
>
> > > StreamExecutor is currently used as the runtime for the vast
> > > majority
> > > of Google's internal GPGPU applications, and a snapshot of it is
> > > included in the open-source TensorFlow_ project, where it serves
> > > as
> > > the GPGPU runtime.
> >
>
> > > .. _TensorFlow: https://www.tensorflow.org
> >
>
> > > It is currently proposed that StreamExecutor itself be
> > > independently
> > > open-sourced. As part of that proposal, this document describes
> > > the
> > > basics of its design and explains why it would fit in well as an
> > > LLVM subproject.
> >
>
> > > -------------------
> >
>
> > > Key points
> >
>
> > > -------------------
> >
>
> > > StreamExecutor:
> >
>
> > > * abstracts the underlying accelerator platform (avoids locking
> > > you
> > > into a single vendor, and lets you write code without thinking
> > > about
> > > which platform you'll be running on).
> >
>
> > > * provides an open-source alternative to the CUDA runtime
> > > library.
> >
>
> > > * gives users a stream management model whose terminology matches
> > > that of the CUDA programming model.
> >
>
> > > * makes use of modern C++ to create a safe, efficient,
> > > easy-to-use
> > > programming interface.
> >
>
> > > StreamExecutor makes it easy to:
> >
>
> > > * move data between host and accelerator (and also between peer
> > > accelerators).
> >
>
> > > * execute data-parallel kernels written in the OpenCL or CUDA
> > > kernel
> > > languages.
> >
>
> > > * inspect the capabilities of a GPU-like device at runtime.
> >
>
> > > * manage multiple devices.
> >
>
> > > --------------------------------
> >
>
> > > Example code snippet
> >
>
> > > --------------------------------
> >
>
> > > The StreamExecutor API uses abstractions that will be familiar to
> > > those who have worked with other GPU APIs: **Streams**,
> > > **Timers**,
> > > and **Kernels**. Its API is *fluent*, meaning that it allows the
> > > user to chain together a sequence of related operations on a
> > > stream,
> > > as in the following code snippet:
> >
>
> > > .. code-block:: c++
> >
>
> > > se::Stream stream(executor);
> >
>
> > > se::Timer timer(executor);
> >
>
> > > stream.InitWithTimer(&timer)
> >
>
> > > .ThenStartTimer(&timer)
> >
>
> > > .ThenLaunch(se::ThreadDim(dim_block_x, dim_block_y),
> >
>
> > > se::BlockDim(dim_grid_x, dim_grid_y),
> >
>
> > > my_kernel,
> >
>
> > > arg0, arg1, arg2)
> >
>
> > > .ThenStopTimer(&timer)
> >
>
> > > .BlockHostUntilDone();
> >
>
> > > The name of the kernel being launched in the snippet above is
> > > `my_kernel` and the arguments being passed to the kernel are
> > > `arg0`,
> > > `arg1`, and `arg2`. Kernels with any number of arguments of any
> > > types are supported, and the number and types of the arguments is
> > > checked at compile time.
> >
>
> > > How does it work?
> >
>
> > > =======================
> >
>
> > > --------------------------------
> >
>
> > > Detailed example
> >
>
> > > --------------------------------
> >
>
> > > The following example shows how we can use StreamExecutor to
> > > create
> > > a
> > > `TypedKernel` instance, associate device code with that instance,
> > > and then use that instance to schedule work on an accelerator
> > > device.
> >
>
> > > .. code-block:: c++
> >
>
> > > #include <cassert>
> >
>
> > > #include "stream_executor.h"
> >
>
> > > namespace se = streamexecutor;
> >
>
> > > // A PTX string defining a CUDA kernel.
> >
>
> > > //
> >
>
> > > // This PTX string represents a kernel that takes two arguments:
> > > an
> > > input value
> >
>
> > > // and an output pointer. The input value is a floating point
> > > number.
> > > The output
> >
>
> > > // value is a pointer to a floating point value in device memory.
> > > The
> > > output
> >
>
> > > // pointer is where the output from the kernel will be written.
> >
>
> > > //
> >
>
> > > // The kernel adds a fixed floating point value to the input and
> > > writes the
> >
>
> > > // result to the output location.
> >
>
> > > static constexpr const char *KERNEL_PTX = R"(
> >
>
> > > .version 3.1
> >
>
> > > .target sm_20
> >
>
> > > .address_size 64
> >
>
> > > .visible .entry add_mystery_value(
> >
>
> > > .param .f32 float_literal,
> >
>
> > > .param .u64 result_loc
> >
>
> > > ) {
> >
>
> > > .reg .u64 %rl<2>;
> >
>
> > > .reg .f32 %f<2>;
> >
>
> > > ld.param.f32 %f1, [float_literal];
> >
>
> > > ld.param.u64 %rl1, [result_loc];
> >
>
> > > add.f32 %f1, %f1, 123.0;
> >
>
> > > st.f32 [%rl1], %f1;
> >
>
> > > ret;
> >
>
> > > }
> >
>
> > > )";
> >
>
> > > // The number of arguments expected by the kernel described in
> >
>
> > > // KERNEL_PTX_TEMPLATE.
> >
>
> > > static constexpr int KERNEL_ARITY = 2;
> >
>
> > > // The name of the kernel described in KERNEL_PTX.
> >
>
> > > static constexpr const char *KERNEL_NAME = "add_mystery_value";
> >
>
> > > // The value added to the input in the kernel described in
> > > KERNEL_PTX.
> >
>
> > > static constexpr float MYSTERY_VALUE = 123.0f;
> >
>
> > > int main(int argc, char *argv[]) {
> >
>
> > > // Get a CUDA Platform object. (Other platforms such as OpenCL
> > > are
> > > also
> >
>
> > > // supported.)
> >
>
> > > se::Platform *platform =
> >
>
> > > se::MultiPlatformManager::PlatformWithName("cuda").ValueOrDie();
> >
>
> > > // Get a StreamExecutor for the chosen Platform. Multiple devices
> > > are
> >
>
> > > // supported, we indicate here that we want to run on device 0.
> >
>
> > > const int device_ordinal = 0;
> >
>
> > > se::StreamExecutor *executor =
> >
>
> > > platform->ExecutorForDevice(device_ordinal).ValueOrDie();
> >
>
> > > // Create a MultiKernelLoaderSpec, which knows where to find the
> > > code
> > > for our
> >
>
> > > // kernel. In this case, the code is stored in memory as a PTX
> > > string.
> >
>
> > > //
> >
>
> > > // Note that the "arity" and name specified here must match
> > > "arity"
> > > and name
> >
>
> > > // of the kernel defined in the PTX string.
> >
>
> > > se::MultiKernelLoaderSpec kernel_loader_spec(KERNEL_ARITY);
> >
>
> > > kernel_loader_spec.AddCudaPtxInMemory(KERNEL_PTX, KERNEL_NAME);
> >
>
> > > // Next create a kernel handle, which we will associate with our
> > > kernel code
> >
>
> > > // (i.e., the PTX string). The type of this handle is a bit
> > > verbose,
> > > so we
> >
>
> > > // create an alias for it.
> >
>
> > > //
> >
>
> > > // This specific type represents a kernel that takes two
> > > arguments:
> > > a
> > > floating
> >
>
> > > // point value and a pointer to a floating point value in device
> > > memory.
> >
>
> > > //
> >
>
> > > // A type like this is nice to have because it enables static
> > > type
> > > checking of
> >
>
> > > // kernel arguments when we enqueue work on a stream.
> >
>
> > > using KernelType = se::TypedKernel<float, se::DeviceMemory<float>
> > > *>;
> >
>
> > > // Now instantiate an object of the specific kernel type we
> > > declared
> > > above.
> >
>
> > > // The kernel object is not yet connected with the device code
> > > that
> > > we want it
> >
>
> > > // to run (that happens with the call to GetKernel below), so it
> > > cannot be
> >
>
> > > // used to execute work on the device yet.
> >
>
> > > //
> >
>
> > > // However, the kernel object is not completely empty when it is
> > > created. From
> >
>
> > > // the StreamExecutor passed into its constructor it knows which
> > > platform it
> >
>
> > > // is targeted for, and it also knows which device it will run
> > > on.
> >
>
> > > KernelType kernel(executor);
> >
>
> > > // Use the MultiKernelLoaderSpec defined above to load the kernel
> > > code onto
> >
>
> > > // the device pointed to by the kernel object and to make that
> > > kernel
> > > object a
> >
>
> > > // handle to the kernel code loaded on that device.
> >
>
> > > //
> >
>
> > > // The MultiKernelLoaderSpec may contain code for several
> > > different
> > > platforms,
> >
>
> > > // but the kernel object has an associated platform, so there is
> > > no
> > > confusion
> >
>
> > > // about which code should be loaded.
> >
>
> > > //
> >
>
> > > // After this call the kernel object can be used to launch its
> > > kernel
> > > on its
> >
>
> > > // device.
> >
>
> > > executor->GetKernel(kernel_loader_spec, &kernel);
> >
>
> > > // Allocate memory in the device memory space to hold the result
> > > of
> > > the kernel
> >
>
> > > // call. This memory will be freed when this object goes out of
> > > scope.
> >
>
> > > se::ScopedDeviceMemory<float> result =
> > > executor->AllocateOwnedScalar<float>();
> >
>
> > > // Create a stream on which to schedule device operations.
> >
>
> > > se::Stream stream(executor);
> >
>
> > > // Schedule a kernel launch on the new stream and block until the
> > > kernel
> >
>
> > > // completes. The kernel call executes asynchronously on the
> > > device,
> > > so we
> >
>
> > > // could do more work on the host before calling
> > > BlockHostUntilDone.
> >
>
> > > const float kernel_input_argument = 42.5f;
> >
>
> > > stream.Init()
> >
>
> > > .ThenLaunch(se::ThreadDim(), se::BlockDim(), kernel,
> >
>
> > > kernel_input_argument, result.ptr())
> >
>
> > > .BlockHostUntilDone();
> >
>
> > > // Copy the result of the kernel call from device back to the
> > > host.
> >
>
> > > float host_result = 0.0f;
> >
>
> > > executor->SynchronousMemcpyD2H(result.cref(),
> > > sizeof(host_result),
> >
>
> > > &host_result);
> >
>
> > > // Verify that the correct result was computed.
> >
>
> > > assert((kernel_input_argument + MYSTERY_VALUE) == host_result);
> >
>
> > > }
> >
>
> > > --------------------------------
> >
>
> > > Kernel Loader Specs
> >
>
> > > --------------------------------
> >
>
> > > An instance of the class `MultiKernelLoaderSpec` is used to
> > > encapsulate knowledge of where the device code for a kernel is
> > > stored and what format it is in. Given a `MultiKernelLoaderSpec`
> > > and
> > > an uninitialized `TypedKernel`, calling the
> > > `StreamExecutor::GetKernel` method will load the code onto the
> > > device and associate the `TypedKernel` instance with that loaded
> > > code. So, in order to initialize a `TypedKernel` instance, it is
> > > first necessary to create a `MultiKernelLoaderSpec`.
> >
>
> > > A `MultiKernelLoaderSpec` supports a different method for adding
> > > device code
> >
>
> > > for each combination of platform, format, and storage location.
> > > The
> > > following
> >
>
> > > table shows some examples:
> >
>
> > > =========== ======= =========== =========================
> >
>
> > > Platform Format Location Setter
> >
>
> > > =========== ======= =========== =========================
> >
>
> > > CUDA PTX disk `AddCudaPtxOnDisk`
> >
>
> > > CUDA PTX memory `AddCudaPtxInMemory`
> >
>
> > > CUDA cubin disk `AddCudaCubinOnDisk`
> >
>
> > > CUDA cubin memory `AddCudaCubinInMemory`
> >
>
> > > OpenCL text disk `AddOpenCLTextOnDisk`
> >
>
> > > OpenCL text memory `AddOpenCLTextInMemory`
> >
>
> > > OpenCL binary disk `AddOpenCLBinaryOnDisk`
> >
>
> > > OpenCL binary memory `AddOpenCLBinaryInMemory`
> >
>
> > > =========== ======= =========== =========================
> >
>
> > > The specific method used in the example is `AddCudaPtxInMemory`,
> > > but
> > > all other methods are used similarly.
> >
>
> > > ------------------------------------
> >
>
> > > Compiler Support for StreamExecutor
> >
>
> > > ------------------------------------
> >
>
> > > General strategies
> >
>
> > > -------------------
> >
>
> > > For illustrative purposes, the PTX code in the example is written
> > > by
> > > hand and appears as a string literal in the source code file, but
> > > it
> > > is far more typical for the kernel code to be expressed in a high
> > > level language like CUDA C++ or OpenCL C and for the device
> > > machine
> > > code to be generated by a compiler.
> >
>
> > > There are several ways we can load compiled device code using
> > > StreamExecutor.
> >
>
> > > One possibility is that the build system could write the compiled
> > > device code to a file on disk. This can then be added to a
> > > `MultiKernelLoaderSpec` by using one of the `OnDisk` setters.
> >
>
> > > Another option is to add a feature to the compiler which embeds
> > > the
> > > compiled device code into the host executable and provides some
> > > symbol (probably with a name based on the name of the kernel)
> > > that
> > > allows the host code to refer to the embedded code data.
> >
>
> > > In fact, as discussed below, in the current use of StreamExecutor
> > > inside Google, the compiler goes even further and generates an
> > > instance of `MultiKernelLoaderSpec` for each kernel. This means
> > > the
> > > application author doesn't have to know anything about how or
> > > where
> > > the compiler decided to store the compiled device code, but
> > > instead
> > > gets a pre-made loader object that handles all those details.
> >
>
> > > Compiler-generated code makes things safe
> >
>
> > > --------------------------------------------
> >
>
> > > Two of the steps in the example above are dangerous because they
> > > lack
> > > static safety checks: instantiating the `MultiKernelLoaderSpec`
> > > and
> > > specializing the `TypedKernel` class template. This section
> > > discusses how compiler support for StreamExecutor can make these
> > > steps safe.
> >
>
> > > Instantiating a `MultiKernelLoaderSpec` requires specifying a
> > > three
> > > things:
> >
>
> > > 1. the kernel *arity* (number of parameters),
> >
>
> > > 2. the kernel name,
> >
>
> > > 3. a string containing the device machine code for the kernel
> > > (either
> > > as assembly, or some sort of object file).
> >
>
> > > The problem with this is that the kernel name and the number of
> > > parameters is already fully determined by the kernel's machine
> > > code.
> > > In the best case scenario the *arity* and name arguments passed
> > > to
> > > the `MultiKernelLoaderSpec` methods match the information in the
> > > machine code and are simply redundant, but in the worst case
> > > these
> > > arguments contradict the information in the machine code and we
> > > get
> > > a runtime error when we try to load the kernel..
> >
>
> > > The second unsafe operation is specifying the kernel parameter
> > > types
> > > as type arguments to the `TypedKernel` class template. The
> > > specified
> > > types must match the types defined in the kernel machine code,
> > > but
> > > again there is no compile-time checking that these types match.
> > > Failure to match these types will result in a runtime error when
> > > the
> > > kernel is launched.
> >
>
> > > We would like the compiler to perform these checks for the
> > > application author, so as to eliminate this source of runtime
> > > errors. In particular, we want the compiler to create an
> > > appropriate
> > > `MultiKernelLoaderSpec` instance and `TypedKernel` specialization
> > > for each kernel definition.
> >
>
> > > One of the main goals of open-sourcing StreamExecutor is to let
> > > us
> > > add this code generation capability to Clang, when the user has
> > > chosen to use StreamExecutor as their runtime for accelerator
> > > operations.
> >
>
> > > Google has been using an internally developed CUDA compiler based
> > > on
> > > Clang called **gpucc** that generates code for StreamExecutor in
> > > this way. The code below shows how the example above would be
> > > written using gpucc to generate the unsafe parts of the code.
> >
>
> > > The kernel is defined in a high-level language (CUDA C++ in this
> > > example) in its own file:
> >
>
> > > .. code-block:: c++
> >
>
> > > // File: add_mystery_value.cu
> >
>
> > > __global__ void add_mystery_value(float input, float *output) {
> >
>
> > > *output = input + 42.0f;
> >
>
> > > }
> >
>
> > > The host code is defined in another file:
> >
>
> > > .. code-block:: c++
> >
>
> > > // File: example_host_code.cc
> >
>
> > > #include <cassert>
> >
>
> > > #include "stream_executor.h"
> >
>
> > > // This header is generated by the gpucc compiler and it contains
> > > the
> >
>
> > > // definitions of gpucc::kernel::AddMysteryValue and
> >
>
> > > // gpucc::spec::add_mystery_value().
> >
>
> > > //
> >
>
> > > // The name of this header file is derived from the name of the
> > > file
> > > containing
> >
>
> > > // the kernel code. The trailing ".cu" is replaced with ".gpu.h".
> >
>
> > > #include "add_mystery_value.gpu.h"
> >
>
> > > namespace se = streamexecutor;
> >
>
> > > int main(int argc, char *argv[]) {
> >
>
> > > se::Platform *platform =
> >
>
> > > se::MultiPlatformManager::PlatformWithName("cuda").ValueOrDie();
> >
>
> > > const int device_ordinal = 0;
> >
>
> > > se::StreamExecutor *executor =
> >
>
> > > platform->ExecutorForDevice(device_ordinal).ValueOrDie();
> >
>
> > > // AddMysteryValue is an instance of TypedKernel generated by
> > > gpucc.
> > > The
> >
>
> > > // template arguments are chosen by the compiler to match the
> > > parameters of
> >
>
> > > // the add_mystery_value kernel.
> >
>
> > > gpucc::kernel::AddMysteryValue kernel(executor);
> >
>
> > > // gpucc::spec::add_mystery_value() is generated by gpucc. It
> > > returns
> > > a
> >
>
> > > // MultiKernelLoaderSpec that knows how to find the compiled code
> > > for
> > > the
> >
>
> > > // add_mystery_value kernel.
> >
>
> > > executor->GetKernel(gpucc::spec::add_mystery_value(), &kernel);
> >
>
> > > se::ScopedDeviceMemory<float> result =
> > > executor->AllocateOwnedScalar<float>();
> >
>
> > > se::Stream stream(executor);
> >
>
> > > const float kernel_input_argument = 42.5f;
> >
>
> > > stream.Init()
> >
>
> > > .ThenLaunch(se::ThreadDim(), se::BlockDim(), kernel,
> >
>
> > > kernel_input_argument, result.ptr())
> >
>
> > > .BlockHostUntilDone();
> >
>
> > > float host_result = 0.0f;
> >
>
> > > executor->SynchronousMemcpyD2H(result.cref(),
> > > sizeof(host_result),
> >
>
> > > &host_result);
> >
>
> > > assert((kernel_input_argument + 42.0f) == host_result);
> >
>
> > > }
> >
>
> > > This support from the compiler makes the use of StreamExecutor
> > > safe
> > > and easy.
> >
>
> > > Compiler support for triple angle bracket kernel launches
> >
>
> > > ----------------------------------------------------------
> >
>
> > > For even greater ease of use, Google's gpucc CUDA compiler also
> > > supports an integrated mode that looks like NVIDIA's `CUDA
> > > programming model`_,which uses triple angle brackets (`<<<>>>`)
> > > to
> > > launch kernels.
> >
>
> > > .. _CUDA programming model:
> > > http://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#kernels
> >
>
> > > .. code-block:: c++
> >
>
> > > :emphasize-lines: 22
> >
>
> > > #include <cassert>
> >
>
> > > #include "stream_executor.h"
> >
>
> > > namespace se = streamexecutor;
> >
>
> > > __global__ void add_mystery_value(float input, float *output) {
> >
>
> > > *output = input + 42.0f;
> >
>
> > > }
> >
>
> > > int main(int argc, char *argv[]) {
> >
>
> > > se::Platform *platform =
> >
>
> > > se::MultiPlatformManager::PlatformWithName("cuda").ValueOrDie();
> >
>
> > > const int device_ordinal = 0;
> >
>
> > > se::StreamExecutor *executor =
> >
>
> > > platform->ExecutorForDevice(device_ordinal).ValueOrDie();
> >
>
> > > se::ScopedDeviceMemory<float> result =
> > > executor->AllocateOwnedScalar<float>();
> >
>
> > > const float kernel_input_argument = 42.5f;
> >
>
> > > add_mystery_value<<<1, 1>>>(kernel_input_argument,
> > > *result.ptr());
> >
>
> > > float host_result = 0.0f;
> >
>
> > > executor->SynchronousMemcpyD2H(result.cref(),
> > > sizeof(host_result),
> >
>
> > > &host_result);
> >
>
> > > assert((kernel_input_argument + 42.0f) == host_result);
> >
>
> > > }
> >
>
> > > Under the hood, gpucc converts the triple angle bracket kernel
> > > call
> > > into a series of calls to the StreamExecutor library similar to
> > > the
> > > calls seen in the previous examples.
> >
>
> > > Clang currently supports the triple angle bracket kernel call
> > > syntax
> > > for CUDA compilation by replacing a triple angle bracket call
> > > with
> > > calls to the NVIDIA CUDA runtime library, but it would be easy to
> > > add a compiler flag to tell Clang to emit calls to the
> > > StreamExecutor library instead. There are several benefits to
> > > supporting this mode of compilation in Clang:
> >
>
> > > .. _benefits-of-streamexecutor:
> >
>
> > > * StreamExecutor is a high-level, modern C++ API, so is easier to
> > > use
> > > and less prone to error than the NVIDIA CUDA runtime and the
> > > OpenCL
> > > runtime.
> >
>
> > > * StreamExecutor will be open-source software, so GPU code will
> > > not
> > > have to depend on opaque binary blobs like the NVIDIA CUDA
> > > runtime
> > > library.
> >
>
> > > * Using StreamExecutor as the runtime would allow for easy
> > > extension
> > > of the triple angle bracket kernel launch syntax to support
> > > different accelerator programming models.
> >
>
> > > Supporting other platforms
> >
>
> > > ===========================
> >
>
> > > StreamExecutor currently supports CUDA and OpenCL platforms
> > > out-of-the-box, but it uses a platform plugin architecture that
> > > makes it easy to add new platforms at any time. The CUDA and
> > > OpenCL
> > > platforms are both implemented as platform plugins in this way,
> > > so
> > > they serve as good examples for future platform developers of how
> > > to
> > > write these kinds of plugins.
> >
>
> > > Canned operations
> >
>
> > > ==================
> >
>
> > > StreamExecutor provides several predefined kernels for common
> > > data-parallel operations. The supported classes of operations
> > > are:
> >
>
> > > * BLAS: basic linear algebra subprograms,
> >
>
> > > * DNN: deep neural networks,
> >
>
> > > * FFT: fast Fourier transforms, and
> >
>
> > > * RNG: random number generation.
> >
>
> > > Here is an example of using a canned operation to perform random
> > > number generation:
> >
>
> > > .. code-block:: c++
> >
>
> > > :emphasize-lines: 12-13,17,34-35
> >
>
> > > #include <array>
> >
>
> > > #include "cuda/cuda_rng.h"
> >
>
> > > #include "stream_executor.h"
> >
>
> > > namespace se = streamexecutor;
> >
>
> > > int main(int argc, char *argv[]) {
> >
>
> > > se::Platform *platform =
> >
>
> > > se::MultiPlatformManager::PlatformWithName("cuda").ValueOrDie();
> >
>
> > > se::PluginConfig plugin_config;
> >
>
> > > plugin_config.SetRng(se::cuda::kCuRandPlugin);
> >
>
> > > const int device_ordinal = 0;
> >
>
> > > se::StreamExecutor *executor =
> >
>
> > > platform->ExecutorForDeviceWithPluginConfig(device_ordinal,
> > > plugin_config)
> >
>
> > > .ValueOrDie();
> >
>
> > > const uint8 seed[] = {0x0, 0x1, 0x2, 0x3, 0x4, 0x5, 0x6, 0x7,
> >
>
> > > 0x8, 0x9, 0xa, 0xb, 0xc, 0xd, 0xe, 0xf};
> >
>
> > > constexpr uint64 random_element_count = 1024;
> >
>
> > > using HostArray = std::array<float, random_element_count>;
> >
>
> > > HostArray host_memory;
> >
>
> > > const size_t data_size = host_memory.size() *
> > > sizeof(HostArray::value_type);
> >
>
> > > se::ScopedDeviceMemory<float> device_memory =
> >
>
> > > executor->AllocateOwnedArray<float>(random_element_count);
> >
>
> > > se::Stream stream(executor);
> >
>
> > > stream.Init()
> >
>
> > > .ThenSetRngSeed(seed, sizeof(seed))
> >
>
> > > .ThenPopulateRandUniform(device_memory.ptr())
> >
>
> > > .BlockHostUntilDone();
> >
>
> > > executor->SynchronousMemcpyD2H(*device_memory.ptr(), data_size,
> >
>
> > > host_memory.data());
> >
>
> > > }
> >
>
> > > Each platform plugin can define its own canned operation plugins
> > > for
> > > these operations or choose to leave any of them unimplemented.
> >
>
> > > Contrast with OpenMP
> >
>
> > > =====================
> >
>
> > > Recent versions of OpenMP also provide a high-level, easy-to-use
> > > interface for running data-parallel workloads on an accelerator
> > > device. One big difference between OpenMP's approach and that of
> > > StreamExecutor is that OpenMP generates both the kernel code that
> > > runs on the device and the host-side code needed to launch the
> > > kernel, whereas StreamExecutor only generates the host-side code.
> > > While the OpenMP model provides the convenience of allowing the
> > > author to write their kernel code in standard C/C++, the
> > > StreamExecutor model allows for the use of any kernel language
> > > (e.g.
> > > CUDA C++ or OpenCL C). This lets authors use platform-specific
> > > features that are only present in platform-specific kernel
> > > definition languages.
> >
>
> > > The philosophy of StreamExecutor is that performance is critical
> > > on
> > > the device, but less so on the host. As a result, no attempt is
> > > made
> > > to use a high-level device abstraction during device code
> > > generation. Instead, the high-level abstraction provided by
> > > StreamExecutor is used only for the host-side code that moves
> > > data
> > > and launches kernels. This host-side work is tedious and is not
> > > performance critical, so it benefits from being wrapped in a
> > > high-level library that can support a wide range of platforms in
> > > an
> > > easily extensible manner.
> >
>
> > > Cooperation with OpenMP
> >
>
> > > ========================
> >
>
> > > The Clang OpenMP community is currently in the process of
> > > `designing
> > > their implementation`_ of offloading support. They will want the
> > > compiler to convert the various standardized target-oriented
> > > OpenMP
> > > pragmas into device code to execute on an accelerator and host
> > > code
> > > to load and run that device code. StreamExecutor may provide a
> > > convenient API for OpenMP to use to generate their host-side
> > > code.
> >
>
> > > .. _designing their implementation:
> > > https://drive.google.com/a/google.com/file/d/0B-jX56_FbGKRM21sYlNYVnB4eFk/view
> >
>
> > > In addition to the :ref:`benefits<benefits-of-streamexecutor>`
> > > that
> > > all users of StreamExecutor enjoy over the alternative host-side
> > > runtime libraries, OpenMP and StreamExecutor may mutually benefit
> > > by
> > > sharing work to support new platforms. If OpenMP makes use of
> > > StreamExecutor, then it should be simple for OpenMP to add
> > > support
> > > for any new platforms that StreamExecutor supports in the future.
> > > Similarly, for any platforms OpenMP would like to target, they
> > > may
> > > add that support in StreamExecutor and take advantage of the
> > > knowledge of platform support in the StreamExecutor community.
> > > The
> > > resulting new platform support would then be available not just
> > > within OpenMP, but also to any user of StreamExecutor.
> >
>
> > > Although OpenMP and StreamExecutor support different programming
> > > models, some of the work they perform under the hood will likely
> > > be
> > > very similar. By sharing code and domain expertise, both projects
> > > will be improved and strengthened as their capabilities are
> > > expanded. The StreamExecutor community looks forward to much
> > > collaboration and discussion with OpenMP about the best places
> > > and
> > > ways to cooperate.
> >
>
> > > _______________________________________________
> >
>
> > > LLVM Developers mailing list
> >
>
> > > llvm-dev at lists.llvm.org
> >
>
> > > http://lists.llvm.org/cgi-bin/mailman/listinfo/llvm-dev
> >
>
> > --
>
> > Hal Finkel
>
> > Assistant Computational Scientist
>
> > Leadership Computing Facility
>
> > Argonne National Laboratory
>
--
Hal Finkel
Assistant Computational Scientist
Leadership Computing Facility
Argonne National Laboratory
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