diff --git a/_sources/examples/basic.rst.txt b/_sources/examples/basic.rst.txt index 8d9106c..becfd89 100644 --- a/_sources/examples/basic.rst.txt +++ b/_sources/examples/basic.rst.txt @@ -30,8 +30,8 @@ Code Explanation :lineno-start: 8 First, we need to define a ``KernelBuilder`` instance. -A ``KernelBuilder`` is essentially a `blueprint` that describes the information required to compile the CUDA kernel. -The constructor takes the name of the kernel function and the `.cu` file where the code is located. +A ``KernelBuilder`` is essentially a ``blueprint`` that describes the information required to compile the CUDA kernel. +The constructor takes the name of the kernel function and the ``.cu`` file where the code is located. Optionally, we can also provide the kernel source as the third parameter. @@ -40,7 +40,7 @@ Optionally, we can also provide the kernel source as the third parameter. :lineno-start: 11 CUDA kernels often have tunable parameters that can impact their performance, such as block size, thread granularity, register usage, and the use of shared memory. -Here, we define two tunable parameters: the number of threads per blocks and the number of elements processed per thread. +Here, we define two tunable parameters: the number of threads per block and the number of elements processed per thread. @@ -48,7 +48,7 @@ Here, we define two tunable parameters: the number of threads per blocks and the :lines: 15-16 :lineno-start: 15 -The values returned by ``tune`` are placeholder objecs. +The values returned by ``tune`` are placeholder objects. These objects can be combined using C++ operators to create new expressions objects. Note that ``elements_per_block`` does not actually contain a specific value; instead, it is an abstract expression that, upon kernel instantiation, is evaluated as the product of ``threads_per_block`` and ``elements_per_thread``. @@ -64,7 +64,7 @@ The following properties are supported: * ``problem_size``: This is an N-dimensional vector that represents the size of the problem. In this case, is one-dimensional and ``kl::arg0`` means that the size is specified as the first kernel argument (`argument 0`). * ``block_size``: A triplet ``(x, y, z)`` representing the block dimensions. -* ``grid_divsor``: This property is used to calculate the size of the grid (i.e., the number of blocks along each axis). For each kernel launch, the problem size is divided by the divisors to calculate the grid size. In other words, this property expresses the number of elements processed per thread block. +* ``grid_divisor``: This property is used to calculate the size of the grid (i.e., the number of blocks along each axis). For each kernel launch, the problem size is divided by the divisors to calculate the grid size. In other words, this property expresses the number of elements processed per thread block. * ``template_args``: This property specifies template arguments, which can be type names and integral values. * ``define``: Define preprocessor constants. * ``shared_memory``: Specify the amount of shared memory required, in bytes. diff --git a/_sources/examples/pragma.rst.txt b/_sources/examples/pragma.rst.txt index 2445872..c86f9a4 100644 --- a/_sources/examples/pragma.rst.txt +++ b/_sources/examples/pragma.rst.txt @@ -2,7 +2,7 @@ Pragma Kernels =========================== In the previous examples, we demonstrated how a tunable kernel can be specified by defining a ``KernelBuilder`` instance in the host-side code. -While this API offers flexiblity, it can be cumbersome and requires keeping the kernel code in CUDA in sync with the host-side code in C++. +While this API offers flexibility, it can be cumbersome and requires keeping the kernel code in CUDA in sync with the host-side code in C++. Kernel Launcher also provides a way to define kernel specifications directly in the CUDA code by using pragma directives to annotate the kernel code. Although this method is less flexible than the ``KernelBuilder`` API, it is much more convenient and suitable for most CUDA kernels. @@ -30,7 +30,7 @@ The kernel contains the following ``pragma`` directives: :lineno-start: 1 The tune directives specify the tunable parameters: ``threads_per_block`` and ``items_per_thread``. -Since ``items_per_thread`` is also the name of the template parameter, so it is passed to the kernel as a compile-time constant via this parameter. +Since ``items_per_thread`` is also the name of the template parameter, it is passed to the kernel as a compile-time constant via this parameter. The value of ``threads_per_block`` is not passed to the kernel but is used by subsequent pragmas. .. literalinclude:: vector_add_annotated.cu @@ -44,7 +44,7 @@ In this case, the constant ``items_per_block`` is defined as the product of ``th :lines: 4-6 :lineno-start: 4 -The ``problem_size`` directive defines the problem size (as discussed in as discussed in :doc:`basic`), ``block_size`` specifies the thread block size, and ``grid_divisor`` specifies how the problem size should be divided to obtain the thread grid size. +The ``problem_size`` directive defines the problem size (as discussed in :doc:`basic`), ``block_size`` specifies the thread block size, and ``grid_divisor`` specifies how the problem size should be divided to obtain the thread grid size. Alternatively, ``grid_size`` can be used to specify the grid size directly. @@ -67,7 +67,7 @@ In this example, the tuning key is ``"vector_add_" + T``, where ``T`` is the nam Host Code --------- -The below code shows how to call the kernel from the host in C++:: +The code below shows how to call the kernel from the host in C++:: #include "kernel_launcher/pragma.h" using namespace kl = kernel_launcher; diff --git a/_sources/examples/registry.rst.txt b/_sources/examples/registry.rst.txt index f3189d5..a3859de 100644 --- a/_sources/examples/registry.rst.txt +++ b/_sources/examples/registry.rst.txt @@ -7,11 +7,11 @@ Kernel Registry .. The kernel registry essentially acts like a global cache of compiled kernels. In the previous example, we saw how to use wisdom files by creating a ``WisdomKernel`` object. -This object will compile the kernel code on the first call and the keep the kernel loaded as long as the object exists. +This object will compile the kernel code on the first call and then keep the kernel loaded as long as the object exists. Typically, one would define the ``WisdomKernel`` object as part of a class or as a global variable. However, in certain scenarios, it is inconvenient or impractical to store ``WisdomKernel`` objects. -In these cases, it is possible to use the ``KernelRegistry``, that essentially acts like a global table of compiled kernel instances. +In these cases, it is possible to use the ``KernelRegistry`` that essentially acts like a global table of compiled kernel instances. Source code @@ -36,8 +36,8 @@ Defining a kernel descriptor :lines: 6-43 :lineno-start: 6 -This part of the code defines a ``IKernelDescriptor``: -a class that encapsulate the information required to compile a kernel. +This part of the code defines an ``IKernelDescriptor``: +a class that encapsulates the information required to compile a kernel. This class should override two methods: - ``build`` to instantiate a ``KernelBuilder``, @@ -64,7 +64,7 @@ kernel is only compiled once and stored in the registry. :lineno-start: 59 Alternatively, it is possible to use the above short-hand syntax. -This syntax also make it is easy to replace the element type ``float`` to some other type such as ``int``:: +This syntax also makes it easy to replace the element type ``float`` with some other type such as ``int``:: kl::launch(VectorAddDescriptor::for_type(), n, dev_C, dev_A, dev_B); @@ -75,4 +75,4 @@ It is even possible to define a templated function that passes type ``T`` on to kl::launch(VectorAddDescriptor::for_type(), n, C, A, B); } -Instead of using the global kernel registery, it is also possible to create local registry by creating a ``KernelRegistry`` instance. +Instead of using the global kernel registry, it is also possible to create a local registry by creating a ``KernelRegistry`` instance. diff --git a/_sources/examples/wisdom.rst.txt b/_sources/examples/wisdom.rst.txt index c7db517..230a4af 100644 --- a/_sources/examples/wisdom.rst.txt +++ b/_sources/examples/wisdom.rst.txt @@ -6,6 +6,7 @@ Wisdom Files In the previous example, we demonstrated how to compile a kernel by providing both a ``KernelBuilder`` instance (describing the `blueprint` for the kernel) and a ``Config`` instance (describing the configuration of the tunable parameters). + However, determining the optimal configuration can often be challenging, as it depends on both the problem size and the specific type of GPU being used. To address this problem, Kernel Launcher provides a solution in the form of **wisdom files** (terminology borrowed from `FFTW `_). @@ -86,7 +87,7 @@ To do so, we need to run the program with the environment variable ``KERNEL_LAUN This generates a file called ``vector_add_1000000.json`` in the directory set by ``set_global_capture_directory``. Alternatively, it is possible to capture several kernels at once by using the wildcard ``*``. -For example, the following command export all kernels that are start with ``vector_``:: +For example, the following command exports all kernels that start with ``vector_``:: $ KERNEL_LAUNCHER_CAPTURE=vector_* ./main diff --git a/_sources/index.rst.txt b/_sources/index.rst.txt index 507617e..cb6a007 100644 --- a/_sources/index.rst.txt +++ b/_sources/index.rst.txt @@ -19,9 +19,9 @@ Kernel Launcher .. image:: /logo.png :width: 670 - :alt: kernel launcher + :alt: Kernel Launcher logo -**Kernel Launcher** is a C++ library that makes it easy to dynamically compile *CUDA* kernels at runtime (using `NVRTC `_) and launching them in a type-safe manner using C++ magic. There are two main reasons for using runtime compilation: +**Kernel Launcher** is a C++ library designed to dynamically compile *CUDA* kernels at runtime (using `NVRTC `_) and to launch them in a type-safe manner using C++ magic. Runtime compilation offers two significant advantages: * Kernels that have tunable parameters (block size, elements per thread, loop unroll factors, etc.) where the optimal configuration depends on dynamic factors such as the GPU type and problem size. @@ -33,12 +33,14 @@ Kernel Tuner Integration .. image:: /kernel_tuner_integration.png :width: 670 - :alt: kernel launcher integration + :alt: Kernel Launcher and Kernel Tuner integration -Kernel Launcher's tight integration with `Kernel Tuner `_ results in highly-tuned kernels, as visualized above. -Kernel Launcher **captures** kernel launches within your application, which are then **tuned** by Kernel Tuner and saved as **wisdom** files. -These files are processed by Kernel Launcher during execution to **compile** the tuned kernel at runtime. +The tight integration of **Kernel Launcher** with `Kernel Tuner `_ ensures that kernels are highly optimized, as illustrated in the image above. +Kernel Launcher can **capture** kernel launches within your application at runtime. +These captured kernels can then be **tuned** by Kernel Tuner and the tuning results are saved as **wisdom** files. +These wisdom files are used by Kernel Launcher during execution to **compile** the tuned kernel at runtime. + See :doc:`examples/wisdom` for an example of how this works in practise. @@ -48,7 +50,8 @@ See :doc:`examples/wisdom` for an example of how this works in practise. Basic Example ============= -This sections hows a basic code example. See :ref:`example` for a more advance example. +This section presents a simple code example illustrating how to use the Kernel Launcher. +For a more detailed example, refer to :ref:`example`. Consider the following CUDA kernel for vector addition. This kernel has a template parameter ``T`` and a tunable parameter ``ELEMENTS_PER_THREAD``. @@ -56,13 +59,13 @@ This kernel has a template parameter ``T`` and a tunable parameter ``ELEMENTS_PE .. literalinclude:: examples/vector_add.cu -The following C++ snippet shows how to use *Kernel Launcher* in host code: +The following C++ snippet demonstrates how to use the Kernel Launcher in the host code: .. literalinclude:: examples/index.cpp -Indices and tables +Indices and Tables ============ * :ref:`genindex` diff --git a/examples/basic.html b/examples/basic.html index 34a3870..06b43c1 100644 --- a/examples/basic.html +++ b/examples/basic.html @@ -172,8 +172,8 @@

Code ExplanationKernelBuilder instance. -A KernelBuilder is essentially a blueprint that describes the information required to compile the CUDA kernel. -The constructor takes the name of the kernel function and the .cu file where the code is located. +A KernelBuilder is essentially a blueprint that describes the information required to compile the CUDA kernel. +The constructor takes the name of the kernel function and the .cu file where the code is located. Optionally, we can also provide the kernel source as the third parameter.

11    // Define tunable parameters 
 12    auto threads_per_block = builder.tune("block_size", {32, 64, 128, 256, 512, 1024});
@@ -181,12 +181,12 @@ 
Code Explanation
15    // Define expressions
 16    auto elements_per_block = threads_per_block * elements_per_thread;
-

The values returned by tune are placeholder objecs. +

The values returned by tune are placeholder objects. These objects can be combined using C++ operators to create new expressions objects. Note that elements_per_block does not actually contain a specific value; instead, it is an abstract expression that, upon kernel instantiation, is evaluated as the product of threads_per_block and elements_per_thread.

@@ -206,7 +206,7 @@

Code Explanation

  • problem_size: This is an N-dimensional vector that represents the size of the problem. In this case, is one-dimensional and kl::arg0 means that the size is specified as the first kernel argument (argument 0).

  • block_size: A triplet (x, y, z) representing the block dimensions.

    • -

  • grid_divsor: This property is used to calculate the size of the grid (i.e., the number of blocks along each axis). For each kernel launch, the problem size is divided by the divisors to calculate the grid size. In other words, this property expresses the number of elements processed per thread block.

    • +

  • grid_divisor: This property is used to calculate the size of the grid (i.e., the number of blocks along each axis). For each kernel launch, the problem size is divided by the divisors to calculate the grid size. In other words, this property expresses the number of elements processed per thread block.

  • template_args: This property specifies template arguments, which can be type names and integral values.

  • define: Define preprocessor constants.

  • shared_memory: Specify the amount of shared memory required, in bytes.

    • diff --git a/examples/pragma.html b/examples/pragma.html index 71d16ec..4041414 100644 --- a/examples/pragma.html +++ b/examples/pragma.html @@ -96,7 +96,7 @@

    Pragma Kernels

    In the previous examples, we demonstrated how a tunable kernel can be specified by defining a KernelBuilder instance in the host-side code. -While this API offers flexiblity, it can be cumbersome and requires keeping the kernel code in CUDA in sync with the host-side code in C++.

    +While this API offers flexibility, it can be cumbersome and requires keeping the kernel code in CUDA in sync with the host-side code in C++.

    Kernel Launcher also provides a way to define kernel specifications directly in the CUDA code by using pragma directives to annotate the kernel code. Although this method is less flexible than the KernelBuilder API, it is much more convenient and suitable for most CUDA kernels.

    @@ -133,7 +133,7 @@

    Code Explanationthreads_per_block and items_per_thread. -Since items_per_thread is also the name of the template parameter, so it is passed to the kernel as a compile-time constant via this parameter. +Since items_per_thread is also the name of the template parameter, it is passed to the kernel as a compile-time constant via this parameter. The value of threads_per_block is not passed to the kernel but is used by subsequent pragmas.

    3#pragma kernel set(items_per_block=threads_per_block * items_per_thread)
    @@ -145,7 +145,7 @@

    Code Explanation6#pragma kernel grid_divisor(items_per_block)

    -

    The problem_size directive defines the problem size (as discussed in as discussed in Basic Example), block_size specifies the thread block size, and grid_divisor specifies how the problem size should be divided to obtain the thread grid size. +

    The problem_size directive defines the problem size (as discussed in Basic Example), block_size specifies the thread block size, and grid_divisor specifies how the problem size should be divided to obtain the thread grid size. Alternatively, grid_size can be used to specify the grid size directly.

    7#pragma kernel buffers(C[n], A[n], B[n])
    @@ -161,7 +161,7 @@

    Code Explanation

    Host Code

    -

    The below code shows how to call the kernel from the host in C++:

    +

    The code below shows how to call the kernel from the host in C++:

    #include "kernel_launcher/pragma.h"
 using namespace kl = kernel_launcher;
 
diff --git a/examples/registry.html b/examples/registry.html
index 6121b51..f93dbe2 100644
--- a/examples/registry.html
+++ b/examples/registry.html
@@ -98,10 +98,10 @@
   
    
 
    Kernel Registry
    
 
    In the previous example, we saw how to use wisdom files by creating a WisdomKernel object.
-This object will compile the kernel code on the first call and the keep the kernel loaded as long as the object exists.
+This object will compile the kernel code on the first call and then keep the kernel loaded as long as the object exists.
 Typically, one would define the WisdomKernel object as part of a class or as a global variable.
    
 
    However, in certain scenarios, it is inconvenient or impractical to store WisdomKernel objects.
-In these cases, it is possible to use the KernelRegistry, that essentially acts like a global table of compiled kernel instances.
    
+In these cases, it is possible to use the KernelRegistry that essentially acts like a global table of compiled kernel instances.
    
 
    
 
    Source code
    
 
    Consider the following code snippet:
    
@@ -218,8 +218,8 @@ 
    Defining a kernel descriptor43};
    -

    This part of the code defines a IKernelDescriptor: -a class that encapsulate the information required to compile a kernel. +

    This part of the code defines an IKernelDescriptor: +a class that encapsulates the information required to compile a kernel. This class should override two methods:

    -

    Instead of using the global kernel registery, it is also possible to create local registry by creating a KernelRegistry instance.

    +

    Instead of using the global kernel registry, it is also possible to create a local registry by creating a KernelRegistry instance.

    diff --git a/examples/wisdom.html b/examples/wisdom.html index f5fb87a..1544e3c 100644 --- a/examples/wisdom.html +++ b/examples/wisdom.html @@ -228,7 +228,7 @@

    Export the kernelvector_add_1000000.json in the directory set by set_global_capture_directory.

    Alternatively, it is possible to capture several kernels at once by using the wildcard *. -For example, the following command export all kernels that are start with vector_:

    +For example, the following command exports all kernels that start with vector_:

    $ KERNEL_LAUNCHER_CAPTURE=vector_* ./main
    diff --git a/index.html b/index.html index a4dd590..3260080 100644 --- a/index.html +++ b/index.html @@ -83,8 +83,8 @@

    Kernel Launcher

    -kernel launcher -

    Kernel Launcher is a C++ library that makes it easy to dynamically compile CUDA kernels at runtime (using NVRTC) and launching them in a type-safe manner using C++ magic. There are two main reasons for using runtime compilation:

    +Kernel Launcher logo +

    Kernel Launcher is a C++ library designed to dynamically compile CUDA kernels at runtime (using NVRTC) and to launch them in a type-safe manner using C++ magic. Runtime compilation offers two significant advantages:

    • Kernels that have tunable parameters (block size, elements per thread, loop unroll factors, etc.) where the optimal configuration depends on dynamic factors such as the GPU type and problem size.

    • Improve performance by injecting runtime values as compile-time constant values into kernel code (dimensions, array strides, weights, etc.).

      • @@ -92,15 +92,17 @@

      Kernel Launcher

      Kernel Tuner Integration

      -kernel launcher integration -

      Kernel Launcher’s tight integration with Kernel Tuner results in highly-tuned kernels, as visualized above. -Kernel Launcher captures kernel launches within your application, which are then tuned by Kernel Tuner and saved as wisdom files. -These files are processed by Kernel Launcher during execution to compile the tuned kernel at runtime.

      +Kernel Launcher and Kernel Tuner integration +

      The tight integration of Kernel Launcher with Kernel Tuner ensures that kernels are highly optimized, as illustrated in the image above. +Kernel Launcher can capture kernel launches within your application at runtime. +These captured kernels can then be tuned by Kernel Tuner and the tuning results are saved as wisdom files. +These wisdom files are used by Kernel Launcher during execution to compile the tuned kernel at runtime.

      See Wisdom Files for an example of how this works in practise.

    Basic Example

    -

    This sections hows a basic code example. See Guides for a more advance example.

    +

    This section presents a simple code example illustrating how to use the Kernel Launcher. +For a more detailed example, refer to Guides.

    Consider the following CUDA kernel for vector addition. This kernel has a template parameter T and a tunable parameter ELEMENTS_PER_THREAD.

     1template <typename T>
@@ -116,7 +118,7 @@ 
    Basic Example11}
    -

    The following C++ snippet shows how to use Kernel Launcher in host code:

    +

    The following C++ snippet demonstrates how to use the Kernel Launcher in the host code:

     1#include "kernel_launcher.h"
  2
  3int main() {
@@ -155,7 +157,7 @@ 
    Basic Example
-
    Indices and tables
    
+
    Indices and Tables
    
 
      
 
    • Index
      • 
 
    • Module Index
      • 
diff --git a/searchindex.js b/searchindex.js
index 8a55e67..1297ea5 100644
--- a/searchindex.js
+++ b/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"docnames": ["api", "api/Compiler", "api/Config", "api/ConfigSpace", "api/CudaArch", "api/CudaContextHandle", "api/CudaDevice", "api/CudaException", "api/CudaSpan", "api/DefaultLoader", "api/DefaultOracle", "api/FileLoader", "api/ICompiler", "api/IKernelDescriptor", "api/IntoKernelArg", "api/Kernel", "api/KernelArg", "api/KernelBuilder", "api/KernelInstance", "api/KernelRegistry", "api/KernelSource", "api/NvrtcCompiler", "api/NvrtcException", "api/Oracle", "api/PragmaKernel", "api/ProblemSize", "api/TemplateArg", "api/TunableParam", "api/TypeInfo", "api/Value", "api/Variable", "api/WisdomKernel", "api/WisdomRecord", "api/WisdomSettings", "api/add_global_capture_pattern", "api/append_global_wisdom_directory", "api/build_pragma_kernel", "api/capture_file_exists", "api/cuda_check", "api/cuda_copy", "api/cuda_span", "api/default_compiler", "api/default_registry", "api/default_wisdom_settings", "api/export_capture_file", "api/into_kernel_arg", "api/launch", 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