Just include src/eventsource.js or src/eventsource.min.js in your page to use the polyfill. Unless a typescript definition file is created for this polyfill, this is how you would use it in an Ionic2 project. It should (in theory) be very similar in an Angular2 project.
sse server-sent-events eventsource event-source polyfillwechat vue admin project
wechat vue wxpy itchat wechat-admin flask celery sse walrus mkdocs bot tuling chatterbot python3The Simd Library is a free open source image processing library, designed for C and C++ programmers. It provides many useful high performance algorithms for image processing such as: pixel format conversion, image scaling and filtration, extraction of statistic information from images, motion detection, object detection (HAAR and LBP classifier cascades) and classification, neural network. The algorithms are optimized with using of different SIMD CPU extensions. In particular the library supports following CPU extensions: SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2, AVX, AVX2 and AVX-512 for x86/x64, VMX(Altivec) and VSX(Power7) for PowerPC (big-endian), NEON for ARM.
simd sse avx neon image-processing altivec c-plus-plus vsx sse2 avx2 ssse3 simd-library sse41 arm powerpc lbp haar-cascade avx512SIMD (Single Instruction, Multiple Data) is a feature of microprocessors that has been available for many years. SIMD instructions perform a single operation on a batch of values at once, and thus provide a way to significantly accelerate code execution. However, these instructions differ between microprocessor vendors and compilers. xsimd provides a unified means for using these features for library authors. Namely, it enables manipulation of batches of numbers with the same arithmetic operators as for single values. It also provides accelerated implementation of common mathematical functions operating on batches.
cpp neon c-plus-plus-11 avx sse simd vectorization avx512 mathematical-functions simd-instructions simd-intrinsicslibsimdpp is a portable header-only zero-overhead C++ low level SIMD library. The library presents a single interface over SIMD instruction sets present in x86, ARM, PowerPC and MIPS architectures. On architectures that support different SIMD instruction sets the library allows the same source code files to be compiled for each SIMD instruction set and then hooked into an internal or third-party dynamic dispatch mechanism. This allows the capabilities of the processor to be queried on runtime and the most efficient implementation to be selected. The library sits somewhere in the middle between programming directly in SIMD intrinsics and even higher-level SIMD libraries. As much control as possible is given to the developer, so that it's possible to exactly predict what code the compiler will generate.
sse avx2 avx512 neon vsx msa altivec simdRecent generations of CPUs, and GPUs in particular, require data-parallel codes for full efficiency. Data parallelism requires that the same sequence of operations is applied to different input data. CPUs and GPUs can thus reduce the necessary hardware for instruction decoding and scheduling in favor of more arithmetic and logic units, which execute the same instructions synchronously. On CPU architectures this is implemented via SIMD registers and instructions. A single SIMD register can store N values and a single SIMD instruction can execute N operations on those values. On GPU architectures N threads run in perfect sync, fed by a single instruction decoder/scheduler. Each thread has local memory and a given index to calculate the offsets in memory for loads and stores. Current C++ compilers can do automatic transformation of scalar codes to SIMD instructions (auto-vectorization). However, the compiler must reconstruct an intrinsic property of the algorithm that was lost when the developer wrote a purely scalar implementation in C++. Consequently, C++ compilers cannot vectorize any given code to its most efficient data-parallel variant. Especially larger data-parallel loops, spanning over multiple functions or even translation units, will often not be transformed into efficient SIMD code.
vectorization parallel simd-vector simd-instructions simd avx c-plus-plus avx512 sse neon cpp portable cpp11 cpp14 cpp17 avx2 simd-programming data-parallel parallel-computingThis SIMD class helps developers to detect the types of SIMD instruction available on users' processor. It supports Intel and AMD CPUs. It is written in C++.
amd intel simd sseA collection of sample code, tutorials and documention to aid in the creation of FeedSync based solutions. FeedSync is a multi-master synchronization algorithm and data extensions that can be implemented in almost any language by using almost any technology.
feedsync sse syncThe implementation of well-known basic algorithms in computer science using a variety of technologies. Useful to compare the raw performance of various languages, runtimes, and hardware-based implementations.
algorithms asm performance-testing processor sseArchAssembler is a .net (c#) library providing the functionalities of an assembler. Target architecture is x86/x64 with streaming SIMD extensions. Target executable file format is Windows Portable Executable (PE).
assembler pdb portable-executable simd sse sse2DirectXMath is an all inline SIMD C++ linear algebra library for use in games and graphics apps
microsoft directx simd neon cpp-library sse avx avx2SIMD (Single Instruction, Multiple Data) is a feature of microprocessors that has been available for many years. SIMD instructions perform a single operation on a batch of values at once, and thus provide a way to significantly accelerate code execution. However, these instructions differ between microprocessor vendors and compilers. xsimd provides a unified means for using these features for library authors. Namely, it enables manipulation of batches of numbers with the same arithmetic operators as for single values. It also provides accelerated implementation of common mathematical functions operating on batches.
simd-intrinsics c-plus-plus-14 vectorization simd cpp avx neon sse avx512 simd-instructions mathematical-functionsWe want to remove the space (' ') and the line feeds characters ('\n', '\r') from a string as fast as possible. To avoid unnecessary allocations, we wish to do the processing in-place.Note that clang seems to give better results than gcc.
ascii bytes simd-programming simd ssse3 sse avxServer-Sent Events "channel" where all messages are broadcasted to all connected clients, history is maintained automatically and server attempts to keep clients alive by sending "keep-alive" packets automatically.
sse channel real-time server-sent-events html5 eventsource messagingconnect middleware for server sent events (EventSource)
connect sse middleware eventsource event source server sent eventsSSE is a client/server implementation for Server Side Events for Golang. Right now functionality is basic, but there is support for multiple separate streams with many clients connected to each. The client is able to specify the stream it wants to connect to by the URL parameter stream.The client exposes a way to connect to an SSE server. The client can also handle multiple events under the same url.
sse server-side-events streams eventsClient is the default client used for requests.GetReq is a function to return a single request. It will be used by notify to get a request and can be replaces if additional configuration is desired on the request. The "Accept" header will necessarily be overwritten.
sse server-sent-eventsHTML5 Server-Sent-Events for Go
sse server-sent-events events httpExpose HTML5 Server Sent Events as an installable appliance on Node.JS http servers; connections are emitted as Writable streams. Create a SSE server that emits connection events on new, successful eventstream connections.
sse eventsource stream writable
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