Improving IP Address Parsing Speed
Your current code for parsing IPv4 addresses is fairly efficient, but it can be further optimized for even greater speed. One approach is to utilize vectorized solutions specifically designed for this task.
Vectorized Solution for Fast IPv4 Parsing
For x86 processors supporting SSE4.1 or SSSE3 instructions, here's a vectorized solution that significantly improves performance:
__m128i shuffleTable[65536]; //can be reduced 256x times, see @IwillnotexistIdonotexist UINT32 MyGetIP(const char *str) { __m128i input = _mm_lddqu_si128((const __m128i*)str); //"192.167.1.3" input = _mm_sub_epi8(input, _mm_set1_epi8('0')); //1 9 2 254 1 6 7 254 1 254 3 208 245 0 8 40 __m128i cmp = input; //...X...X.X.XX... (signs) UINT32 mask = _mm_movemask_epi8(cmp); //6792 - magic index __m128i shuf = shuffleTable[mask]; //10 -1 -1 -1 8 -1 -1 -1 6 5 4 -1 2 1 0 -1 __m128i arr = _mm_shuffle_epi8(input, shuf); //3 0 0 0 | 1 0 0 0 | 7 6 1 0 | 2 9 1 0 __m128i coeffs = _mm_set_epi8(0, 100, 10, 1, 0, 100, 10, 1, 0, 100, 10, 1, 0, 100, 10, 1); __m128i prod = _mm_maddubs_epi16(coeffs, arr); //3 0 | 1 0 | 67 100 | 92 100 prod = _mm_hadd_epi16(prod, prod); //3 | 1 | 167 | 192 | ? | ? | ? | ? __m128i imm = _mm_set_epi8(-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 6, 4, 2, 0); prod = _mm_shuffle_epi8(prod, imm); //3 1 167 192 0 0 0 0 0 0 0 0 0 0 0 0 return _mm_extract_epi32(prod, 0); // return (UINT32(_mm_extract_epi16(prod, 1)) <h3 id="Shuffle-Table-Pre-Calculation">Shuffle Table Pre-Calculation</h3><p>To utilize this vectorized solution effectively, it requires a pre-calculated shuffle table, shuffleTable, that can be generated as follows:</p><pre class="brush:php;toolbar:false">void MyInit() { memset(shuffleTable, -1, sizeof(shuffleTable)); int len[4]; for (len[0] = 1; len[0] <h3 id="Performance-Benchmark">Performance Benchmark</h3><p>On an Ivy Bridge processor, the vectorized solution demonstrates impressive performance, processing 336 million addresses per second. This is approximately 7.8 times faster than the code provided in the original question.</p>
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C is not dead, but has flourished in many key areas: 1) game development, 2) system programming, 3) high-performance computing, 4) browsers and network applications, C is still the mainstream choice, showing its strong vitality and application scenarios.

The main differences between C# and C are syntax, memory management and performance: 1) C# syntax is modern, supports lambda and LINQ, and C retains C features and supports templates. 2) C# automatically manages memory, C needs to be managed manually. 3) C performance is better than C#, but C# performance is also being optimized.

You can use the TinyXML, Pugixml, or libxml2 libraries to process XML data in C. 1) Parse XML files: Use DOM or SAX methods, DOM is suitable for small files, and SAX is suitable for large files. 2) Generate XML file: convert the data structure into XML format and write to the file. Through these steps, XML data can be effectively managed and manipulated.

Working with XML data structures in C can use the TinyXML or pugixml library. 1) Use the pugixml library to parse and generate XML files. 2) Handle complex nested XML elements, such as book information. 3) Optimize XML processing code, and it is recommended to use efficient libraries and streaming parsing. Through these steps, XML data can be processed efficiently.

C still dominates performance optimization because its low-level memory management and efficient execution capabilities make it indispensable in game development, financial transaction systems and embedded systems. Specifically, it is manifested as: 1) In game development, C's low-level memory management and efficient execution capabilities make it the preferred language for game engine development; 2) In financial transaction systems, C's performance advantages ensure extremely low latency and high throughput; 3) In embedded systems, C's low-level memory management and efficient execution capabilities make it very popular in resource-constrained environments.

The choice of C XML framework should be based on project requirements. 1) TinyXML is suitable for resource-constrained environments, 2) pugixml is suitable for high-performance requirements, 3) Xerces-C supports complex XMLSchema verification, and performance, ease of use and licenses must be considered when choosing.

C# is suitable for projects that require development efficiency and type safety, while C is suitable for projects that require high performance and hardware control. 1) C# provides garbage collection and LINQ, suitable for enterprise applications and Windows development. 2)C is known for its high performance and underlying control, and is widely used in gaming and system programming.

C code optimization can be achieved through the following strategies: 1. Manually manage memory for optimization use; 2. Write code that complies with compiler optimization rules; 3. Select appropriate algorithms and data structures; 4. Use inline functions to reduce call overhead; 5. Apply template metaprogramming to optimize at compile time; 6. Avoid unnecessary copying, use moving semantics and reference parameters; 7. Use const correctly to help compiler optimization; 8. Select appropriate data structures, such as std::vector.


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SecLists is the ultimate security tester's companion. It is a collection of various types of lists that are frequently used during security assessments, all in one place. SecLists helps make security testing more efficient and productive by conveniently providing all the lists a security tester might need. List types include usernames, passwords, URLs, fuzzing payloads, sensitive data patterns, web shells, and more. The tester can simply pull this repository onto a new test machine and he will have access to every type of list he needs.
