Template Metaprogramming C Pdf Download
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Description:C++ template metaprogramming is ideal for performing automatic compile-time optimization. With this example-driven ebook, you'll learn how improved metaprogramming techniques in C++11 and C++14 can help you avoid a lot of mistakes and tedious work by making the compiler work for you.
Do you work a lot with templates and meta-programmingWith C++17 we get a few nice improvements: some are quite small, butalso there are notable features as well! All in all, the additionsshould significantly improve writing template code.
Do you often use make functions to construct a templated object(like std::make_pair)With C++17 you can forget about (most of) them and just use a regularconstructor :)That also means that a lot of your code - those make functions cannow be removed.
This is another part of the strategy to use auto everywhere. WithC++11 and C++14 you can use it to automatically deduce variables or evenreturn types, plus there are also generic lambdas. Now you can also useit for deducing non-type template parameters.
A promising HPC programming approach leverages C++ template libraries, which hide the details of the underlying infrastructure from application experts. Implementations of this principle include Kokkos [11] from Sandia National Laboratories and the RAJA portability layer [12]. A lower level of abstraction, which is based on similar technology, is provided by the OCCA library [13].
Details on our research into various potential sources of overhead and their mitigation using state-of-the-art API design and programming techniques. This includes concept-based typing to eliminate the need for type erasure, as well as compile-time metaprogramming to maximize automatic kernel fusion opportunities.
Listing 5 shows a simple Celerity HLA example. In all HLA examples, in the interest of readability, a preamble including and is assumed. Note that the code closely resembles the C++ ranges and views API illustrated in listing 4 with two additions: first, as with every SYCL kernel submission, a kernel name has to be specified. Therefore, is passed an explicit template type argument . Note that this ceases to be required with SYCL 2020 [18], which further simplifies Celerity HLA usage (i.e. eliminating in the example). We have chosen to include these classes in the example code presented in this paper to maintain its compatibility with existing SYCL 1.2 implementations.
Listing 6 shows an example of using to request slice range mapping of the corresponding input buffers to compute the product of two square matrices. The template arguments of define the data type on which to operate and the desired dimension of the slice, as illustrated in Fig. 2. Since like provide iterators for traversing their respective ranges, the column-row inner product can be conveniently computed using the algorithm from the standard library.
Consequently, with C++20 compiler support maturing, a completely different approach leveraging concepts was explored. Instead of defining a concrete type as parameter for the kernel functor, a concept is specified which constraints the set of types which are accepted. Conceptually, this is the same as having a template function and using to impose restrictions on the template parameter.
In Listing 9 we can see two functions, each constrained to accept only integral types (e.g. ). The first one uses the traditional way of employing in conjunction with . In this case, is a template meta-function which has the effect of removing the function from the overload set at a given call site if the template parameter is instantiated with a concrete type which does not fulfill the stated condition (). Detailing the mechanisms involved in this process goes beyond the scope of this work, and we would like to refer readers to the existing literature on template metaprogramming [19].
Listing 11 shows the concepts variant of listing 6. Here, the multiply kernel takes two parameters which are both constrained using the concept. Passing this functor to an algorithm basically tells the runtime the following: \"Provide me with some type that satisfies the concept and thus contains a Celerity accessor with slice range-mapping.\". So, rather than requesting an explicit type we specify what features the requested type has to support. This allows the runtime to provide the kernel with a type that holds the required Celerity accessor as-is, without any type erasure involved. Performance-wise this means no branching and hence no diverging instruction paths. However, from runtime perspective, detecting which concept was specified is significantly more involved than with the type erasure approach. With type erasure, detecting the accessor type boils down to checking if the parameter type equals a certain type. With concepts, the library now has to probe the kernel functor with a fixed set of types. Depending on the compile-time information for which of those types the kernel functor is invocable, it can infer the access concept and create the matching Celerity accessor. This approach simplifies the function signature of the kernel functor even further by discarding the data type from the accessor template parameters. However, since there is no way of specifying the dimension of the slice in the concept type, this configuration has to be provided via a method inside the kernel functor.
Note how in listing 11 the range adaptor which takes two input buffers is now created using instead of . This is necessary because it is not possible to determine the arity of a function template without having valid input argument types. Since the function call operator of the kernel functor with its concept-constrained input types is a function template, this also applies here. This in turn means that at the time the operation is packaged there is no way of telling if the kernel functor takes one or two arguments. Thus we need to discern between transformation (one input) and zipping (two inputs) at the call site using two distinct function templates.
In the first category, the history of C++ expression templates is of particular note. Introduced by Haney et al. [20], they were developed into one of the most powerful tools for providing very high levels of abstraction with minimal overhead [21], with Chen et al. initially applying these methods to GPU computing using CUDA [22].
Esterie et al. [23] introduced a numerical template toolbox based on an architecture-aware domain engineering method for reusable algorithmic libraries. Their library NT\\(^2\\) follows a substantially different interface design philosophy compared to Celerity HLA, with the goal being relatively simple porting from Matlab code, while our API design seeks to match the expectations of and appear familiar to C++20 programmers.
Expression templates are frequently used in concert with skeleton-based APIs in order to provide a high level of abstraction in C++ EDSLs. Early implementations of this general concept include the Quaff library by Falcou et al. [24], as well as the Orléans Skeleton Library developed by Noman and Loulergue [25].
Matsuzaki et al. [26] developed a parallel skeleton library for distributed memory environments which supports kernel fusion for list skeletons via an expression template mechanism, but this work was not targeting GPUs or accelerators. Shigeyuki et al. [27] provided an early application of algorithmic skeletons to GPU computing. As was common at the time of this work, only CUDA-based GPU platforms are supported, and the API is still lower level than more recent approaches. More recently, Ernstsson et al. [28] developed an extension to the SkePU skeleton programming model which lazily records the lineage of skeleton invocations, and applies tiling once partial results are actually required by the program.
Looking beyond the rich history of expression templates and skeleton-based APIs, we will now discuss some projects which are closer to Celerity HLA in terms of target platforms and underlying GPU computing technologies.
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