API Design For C

API Design For C

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As for which method is proper api design, it's done both ways in the C standard library. strdup() and stdio uses the second method while sprintf and strcat use the first method. Personally I prefer the second method (or third) unless 1) I know I will never need to realloc and 2) I expect the lifetime of my objects to be short and thus using the stack is very convienent

Right now, this design, combined with a way to generate the dispatch tables, looks to me like the way to go in 2016 and beyond. You get an easy to use API for clients, a lot of freedom to build things on top, while keeping all of the advantages of plain C APIs like portability.

What are some flaws that drive you nuts in C APIs (including standard libraries, third party libraries, and headers inside of a project) The goal is to identify API design pitfalls in C, so people writing new C libraries can learn from mistakes of the past.

There are no absolutes in API design: you cannot apply a fixed set of rules to every situation. However, while there may be individual cases where you decide that it is best for your project to deviate from certain advice in this chapter, you should do so only after reasoned and considered judgement.

Model the key objects: an API should also model the key objects for the problem domain. This process is often called object-oriented design or object modeling because it aims to describe the hierarchy of objects in the specific problem domain.

In term of good API design, you should never make member variables public. If data members form part of the logical interface of the API, then you should instead provide getter and/or setter methods that provide indirect access to the member variables.

This is a two-way dependency between GUI and Client and a tightly coupled design. A solution for it could be to have the GUI register interest in updates from the Client. There are several ways this can be done, such as callbacks, observers, and notifications.

This is a software design pattern where an object maintains a list of its dependent objects (observers) and notifies them by calling one of their methods. This will be presented in the next part of patterns.

A well-designed API should be stable and future proof. In this context, stable does not necessarily mean that the API never changes, but rather that the interface should be versioned and should not change incompatibly from one version to the next. Related to this, the term future proof means that an API should be designed to be extensible so that it can be evolved elegantly rather than changed chaotically.

Our C++ circular buffer mimics much of the logic from the C implementation, but results in a much cleaner and more reusable design. Also, the C++ buffer utilizes std::mutex to provide a thread-safe implementation.

The database is designed to store \"unstructured data\", with no pre-defined lengthor meaning. As a result, the datum is the C equivalent of a Rust slice: a bunchof bytes, and a count of how many there are. The main difference is that there isno type information, which is what void indicates.

The programmer must read and understand the API documentation.While some consider that par for the course in C, a good API design can mitigatethis risk. The POSIX API for DBM did this by consolidating the ownership ofthe iterator with its parent:

An application designer may want to move to 'Multiple Sessions, One Context' if there is a need to prioritize messages where higher value messages maybe sent/received across different Sessions, for example through different TCP connections. This approach can potentially increase throughput as well. This means that it may be necessary to forward received messages to downstream application internal queues such that messages are processed by additional application message processing threads. All received messages can be processed by the same message and event callback functions, or Session specific ones by creating additional callbacks.

With 'Multiple Sessions, Multiple Contexts', a designer can reduce the Context Thread processing burden of the 'Multiple Sessions, One Context' model where all Sessions must wait in the select loop before being processed. In this model, each Session can be separated into its own Context thread, and therefore enhance the processing performances that OS multi-threading provides. However, due to the increased number of threads, this approach requires expansive thread context switching, hence places more burden on the CPU and is more resource intensive.

The design of commonly-used libraries has a large impact on theoverall feel of a programming language. Great libraries feel like anextension of the language itself, and consistency across librarieselevates the overall development experience. To aid in theconstruction of great Swift libraries, one of the major goals forSwift 3 is to define a set of API design guidelinesand to apply those design guidelines consistently.

Swift Standard Library: The entire Swift standard library isbeing reviewed and updated to follow the Swift API designguidelines. The actual work is being performed on theswift-3-api-guidelines branch of the Swiftrepository.

Imported Objective-C APIs: The translation of Objective-C APIsinto Swift is being updated to make Objective-C APIs better matchthe Swift API design guidelines, using a variety of heuristics. TheBetter Translation of Objective-C APIs into Swiftproposal describes how this transformationis done. Because this approach naturally involves a number ofheuristics, we track its effects on the Cocoa and Cocoa Touchframeworks, as well as Swift code using those frameworks. The Swift3 API Design Guidelines Reviewrepository provides a way to see howthis automatic translation affects Swift code that uses Cocoa andCocoa Touch. Specific Objective-C APIs that translate poorly intoSwift will then be annotated (for example, with NS_SWIFT_NAME) to improvethe resulting Swift code. While this change primarily impacts Appleplatforms (where Swift uses the Objective-C runtime), it also has adirect impact on the cross-platform Swift corelibraries that provide the same APIs as Objective-Cframeworks.

The design and implementation of the Celerity High-level API, providing a very high level of abstraction and a functional programming style for targeting clusters of GPUs, which was previously only available for CPUs on single shared-memory nodes.

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.

The remainder of this paper is structured as follows. In Sect. 2, we provide some background information on SYCL and Celerity, as well as the new C++20 standard library features for working with collections, which served as an inspiration for the Celerity HLA. Section 3 describes our core contributions, including the Celerity HLA API design and implementation featuring concept-based typing and compile-time fusion of operations into single kernels. In Sect. 4 we evaluate our implementation and demonstrate its low performance overhead compared to more verbose options. We also investigate the impact of some specific implementation choices and optimizations. Finally, Sect. 5 discusses some related work and Sect. 6 concludes the paper.

The functional approach in Listing 4 achieves the optimal run time properties of Listing 2 while being even more concise than Listing 3 by allowing range adaptions to be composed and evaluated lazily upon iteration in a single pass. This functional style of processing ranges of elements is yet to be adopted widely in the C++ landscape, but with the standardization in C++20 it is very likely to gain broad adoption in the future. It has been chosen as the model for designing the celerity high-level API because it fits our requirements very well: it provides optimal run time complexity while still being clear and descriptive.

Given the additional constraints imposed on GPU kernel code, as well as the requirements for dealing with distributed memory, achieving performance comparable to a low level implementation in Celerity HLA while maintaining programmability and functional composition requires very specific API design choices and researching various implementation alternatives and optimizations.

The significant advantages in terms of code succinctness, readability, and composition conferred by the Celerity HLA would be relatively meaningless if they came at a large general loss in performance potential. Therefore, we provide a set of benchmarks in this section demonstrating the performance of our approach, and analyzing the impact of our design choices and optimizations.

The introduction already covers some related work necessary to establish the larger context of the Celerity HLA, including low-level accelerator and networking APIs such as CUDA, OpenCL, OpenMP and MPI, as well as popular runtime systems and abstraction layers such as StarPU, OmpSs, Kokkos and RAJA. In this section, we want to focus on three additional types of related work: those which introduce techniques leveraged in the design and implementation of the Celerity HLA, skeleton-based libraries and frameworks, and some additional projects which are less widely used but closer in nature to our work than the popular APIs covered in Sect. 1.

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.

Huynh et al. [29] presented a high-level framework targeting streaming applications which maps these applications to multiple GPUs. Their work focuses specifically on the partitioning and communication challenges involved in this type of application. Crucially, they focus on a set of GPUs connected to a single host, while the Celerity HLA system is designed for leveraging distributed memory clusters of GPUs using an underlying MPI layer. 59ce067264

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