// Enjoy the original code without GPU requirements.
// It combines CPU-based optimizations with OpenMP for acceleration.
// Thanks to xAI for scrobbling some information which could be useful in a Internet 3.0 position.
// Adi Protocol Internet 3.0 on ARMv7-A/9 & Intel & AMD
#include <iostream>
#include <vector>
#include <string>
#include <map>
#include <memory>
#include <cmath>
#include <numeric>
#include <algorithm>
#include <stdexcept>
#include <thread>
#include <mutex>
#include <execution> // For parallel algorithms
#include <omp.h> // For OpenMP
#include <bit> // For std::endian (C++20)
#include <cstring> // For std::memcpy
// For runtime CPU feature detection
#ifdef __x86_64__
#include <immintrin.h> // For AVX2/AVX-512 intrinsics
#include <cpuid.h> // For CPUID detection
#elif defined(__aarch64__)
#include <sys/auxv.h>
#include <asm/hwcap.h>
#include <arm_neon.h> // For NEON fallback if needed
#include <arm_sve.h> // For SVE
#endif
// For SIMD JSON parsing
#include "simdjson.h"
using namespace simdjson;
// For HTTP server (cpp-httplib, header-only)
#define CPPHTTPLIB_OPENSSL_SUPPORT
#include "httplib.h"
// --- Common Constants ---
const int OPERATION_INTERPOLATE = 0;
const int OPERATION_DIFFERENTIATE = 1;
const int OPERATION_CALCULATE_GRADIENT_1D = 2;
const int OPERATION_HYPERBOLIC_INTERCEPT_HANDLER = 3;
const int OPERATION_INTEGRATE = 4;
const int OPERATION_INTEGRATE_ND = 5;
const int OPERATION_WORKFLOW = 6;
// --- Helper Functions ---
double calculate_arcsecant(double val) {
if (std::abs(val) < 1.0) {
return NAN;
}
return std::acos(1.0 / val);
}
// Runtime detection functions
bool has_avx2_support() {
#ifdef __x86_64__
unsigned int eax, ebx, ecx, edx;
__cpuid(1, eax, ebx, ecx, edx);
// Check for AVX (ecx bit 28) and AVX2 (ebx bit 5)
return (ecx & (1 << 28)) && __cpuid_count(7, 0, eax, ebx, ecx, edx) && (ebx & (1 << 5));
#else
return false;
#endif
}
bool has_avx512_support() {
#ifdef __x86_64__
unsigned int eax, ebx, ecx, edx;
__cpuid_count(7, 0, eax, ebx, ecx, edx);
// Check for AVX-512F bit
return (ebx & (1 << 16));
#else
return false;
#endif
}
bool has_sve_support() {
#ifdef __aarch64__
long hwcaps = getauxval(AT_HWCAP);
return (hwcaps & HWCAP_SVE) != 0;
#else
return false;
#endif
}
// --- Portable Packing/Unpacking with Endian Awareness ---
std::string _pack_data(const std::vector<double>& data) {
std::string binary_data(data.size() * sizeof(double), '\0');
const char* src = reinterpret_cast<const char*>(data.data());
char* dst = &binary_data[0];
if (std::endian::native == std::endian::big) {
// If big-endian, swap bytes for little-endian network standard
for (size_t i = 0; i < data.size(); ++i) {
uint64_t val;
std::memcpy(&val, src + i * sizeof(double), sizeof(double));
val = __builtin_bswap64(val);
std::memcpy(dst + i * sizeof(double), &val, sizeof(double));
}
} else {
std::memcpy(dst, src, binary_data.size());
}
return binary_data;
}
std::vector<double> _unpack_data(const std::string& binary_data) {
std::vector<double> data(binary_data.size() / sizeof(double));
const char* src = binary_data.data();
char* dst = reinterpret_cast<char*>(data.data());
if (std::endian::native == std::endian::big) {
// Swap back from little-endian
for (size_t i = 0; i < data.size(); ++i) {
uint64_t val;
std::memcpy(&val, src + i * sizeof(double), sizeof(double));
val = __builtin_bswap64(val);
std::memcpy(dst + i * sizeof(double), &val, sizeof(double));
}
} else {
std::memcpy(dst, src, binary_data.size());
}
return data;
}
// --- Optimized Eigenvalue Packing with Cross-Arch SIMD ---
std::vector<double> pack_eigenvalue_data(const std::vector<double>& eigenvalues) {
std::vector<double> packed_data(eigenvalues.size());
if (has_avx512_support()) {
std::cout << "Using AVX-512 optimization." << std::endl;
#ifdef __x86_64__
size_t i = 0;
const size_t vec_size = 8; // AVX-512: 8 doubles
__m512d one = _mm512_set1_pd(1.0);
for (; i + vec_size <= eigenvalues.size(); i += vec_size) {
__m512d vals = _mm512_loadu_pd(&eigenvalues[i]);
__m512d abs_vals = _mm512_abs_pd(vals);
__mmask8 mask_ge_one = _mm512_cmp_pd_mask(abs_vals, one, _CMP_GE_OQ);
__m512d recip = _mm512_div_pd(one, vals);
__m512d arcsec = _mm512_acos_pd(recip);
__m512d result = _mm512_mask_blend_pd(mask_ge_one, arcsec, vals);
_mm512_storeu_pd(&packed_data[i], result);
}
// Scalar remainder
for (; i < eigenvalues.size(); ++i) {
double val = eigenvalues[i];
packed_data[i] = (std::abs(val) >= 1.0) ? calculate_arcsecant(val) : val;
}
#endif
} else if (has_avx2_support()) {
std::cout << "Using AVX2 optimization." << std::endl;
#ifdef __x86_64__
size_t i = 0;
const size_t vec_size = 4; // AVX2: 4 doubles
__m256d one = _mm256_set1_pd(1.0);
for (; i + vec_size <= eigenvalues.size(); i += vec_size) {
__m256d vals = _mm256_loadu_pd(&eigenvalues[i]);
__m256d abs_vals = _mm256_max_pd(vals, _mm256_sub_pd(_mm256_setzero_pd(), vals));
__m256d mask_ge_one = _mm256_cmp_pd(abs_vals, one, _CMP_GE_OQ);
__m256d recip = _mm256_div_pd(one, vals);
__m256d arcsec = _mm256_acos_pd(recip);
__m256d result = _mm256_blendv_pd(vals, arcsec, mask_ge_one);
_mm256_storeu_pd(&packed_data[i], result);
}
// Scalar remainder
for (; i < eigenvalues.size(); ++i) {
double val = eigenvalues[i];
packed_data[i] = (std::abs(val) >= 1.0) ? calculate_arcsecant(val) : val;
}
#endif
} else if (has_sve_support()) {
std::cout << "Using ARM SVE optimization." << std::endl;
#ifdef __ARM_FEATURE_SVE
size_t i = 0;
svfloat64_t one = svdup_f64(1.0);
for (; i + svcntd() <= eigenvalues.size(); i += svcntd()) {
svfloat64_t sv_eigenvalues = svld1_f64(svptrue_b64(), &eigenvalues[i]);
svfloat64_t sv_abs_val = svabs_f64_z(svptrue_b64(), sv_eigenvalues);
svbool_t p_ge_one = svcmpge_f64(svptrue_b64(), sv_abs_val, one);
svfloat64_t sv_recip = svdiv_f64_z(svptrue_b64(), one, sv_eigenvalues);
svfloat64_t sv_arcsec = svacos_f64_z(svptrue_b64(), sv_recip);
svfloat64_t sv_result = svsel_f64(p_ge_one, sv_arcsec, sv_eigenvalues);
svst1_f64(svptrue_b64(), &packed_data[i], sv_result);
}
// Scalar remainder
for (; i < eigenvalues.size(); ++i) {
double val = eigenvalues[i];
packed_data[i] = (std::abs(val) >= 1.0) ? calculate_arcsecant(val) : val;
}
#endif
} else {
std::cout << "No advanced SIMD detected, using parallel scalar loop." << std::endl;
#pragma omp parallel for
for (size_t i = 0; i < eigenvalues.size(); ++i) {
double val = eigenvalues[i];
packed_data[i] = (std::abs(val) >= 1.0) ? calculate_arcsecant(val) : val;
}
}
return packed_data;
}
// --- Optimized Interpolation with OpenMP Parallelism ---
std::vector<double> hyperbolic_parabolic_interpolation(
const std::map<std::string, std::vector<double>>& data_dict,
const std::vector<double>& x_interp) {
std::vector<std::vector<double>> all_fx_data;
std::vector<std::vector<double>> all_fy_data;
for (const auto& pair : data_dict) {
if (pair.first.find("fx") == 0) {
all_fx_data.push_back(pair.second);
} else if (pair.first.find("fy") == 0) {
all_fy_data.push_back(pair.second);
}
}
if (all_fx_data.size() != all_fy_data.size() || x_interp.empty()) {
throw std::invalid_argument("Invalid data for interpolation.");
}
std::vector<double> all_interp_y;
all_interp_y.reserve(all_fx_data.size() * x_interp.size());
#pragma omp parallel for
for (size_t i = 0; i < all_fx_data.size(); ++i) {
const auto& fx = all_fx_data[i];
const auto& fy = all_fy_data[i];
if (fx.size() != fy.size() || fx.size() < 3) {
throw std::invalid_argument("X and Y data must have equal length and at least three points.");
}
std::vector<double> local_interp_y;
local_interp_y.reserve(x_interp.size());
for (double x : x_interp) {
std::vector<std::pair<double, double>> points(fx.size());
for (size_t j = 0; j < fx.size(); ++j) {
points[j] = {std::abs(fx[j] - x), fx[j]};
}
std::sort(points.begin(), points.end());
double x1 = points[0].second, x2 = points[1].second, x3 = points[2].second;
auto find_y = [&](double search_x) {
for (size_t k = 0; k < fx.size(); ++k) {
if (fx[k] == search_x) return fy[k];
}
return 0.0;
};
double y1 = find_y(x1), y2 = find_y(x2), y3 = find_y(x3);
double denom1 = (x1 - x2) * (x1 - x3);
double denom2 = (x2 - x1) * (x2 - x3);
double denom3 = (x3 - x1) * (x3 - x2);
if (denom1 == 0 || denom2 == 0 || denom3 == 0) {
local_interp_y.push_back(0.0); // Avoid div-by-zero
continue;
}
double L1 = ((x - x2) * (x - x3)) / denom1;
double L2 = ((x - x1) * (x - x3)) / denom2;
double L3 = ((x - x1) * (x - x2)) / denom3;
local_interp_y.push_back(L1 * y1 + L2 * y2 + L3 * y3);
}
#pragma omp critical
all_interp_y.insert(all_interp_y.end(), local_interp_y.begin(), local_interp_y.end());
}
return all_interp_y;
}
// --- Optimized Gradient with Parallel Execution ---
std::vector<double> calculate_gradient_1d(const std::vector<double>& data) {
if (data.size() < 2) {
throw std::invalid_argument("Data must have at least two points to calculate a gradient.");
}
std::vector<double> gradient(data.size() - 1);
std::adjacent_difference(std::execution::par, data.begin() + 1, data.end(), gradient.begin());
return gradient;
}
// --- Eigenvalue Handler with Parallelism ---
std::vector<double> handle_eigenvalue_reference_op(const std::vector<double>& packed_data) {
double sum = std::accumulate(std::execution::par, packed_data.begin(), packed_data.end(), 0.0);
double mean_value = sum / packed_data.size();
std::cout << "Server received 'eigenvalue packed radices' data." << std::endl;
std::cout << "Calculated mean medium: " << mean_value << std::endl;
std::vector<double> result(5);
#pragma omp parallel for
for (int i = 0; i < 5; ++i) {
result[i] = mean_value * (i + 1);
}
return result;
}
// --- Workflow Handler with simdjson ---
std::vector<double> handle_workflow(ondemand::document& workflow_doc) {
auto data_store = std::make_unique<std::map<std::string, std::vector<double>>>();
std::vector<double> final_result;
for (auto& step : workflow_doc.get_array()) {
std::string_view operation = step["operation_type"];
std::vector<double> input_data_vec;
auto input_data = step["input_data"];
std::string_view input_type = input_data["type"];
if (input_type == "direct") {
if (operation == "INTERPOLATE") {
auto fx_data_list = input_data["fx_data"].get_array();
auto fy_data_list = input_data["fy_data"].get_array();
std::map<std::string, std::vector<double>> interpolation_data;
size_t idx = 0;
for (auto fx : fx_data_list) {
std::vector<double> fx_vec;
for (auto val : fx.get_array()) fx_vec.push_back(double(val));
interpolation_data["fx" + std::to_string(idx)] = std::move(fx_vec);
auto fy = fy_data_list.at(idx).get_array();
std::vector<double> fy_vec;
for (auto val : fy) fy_vec.push_back(double(val));
interpolation_data["fy" + std::to_string(idx)] = std::move(fy_vec);
++idx;
}
std::vector<double> x_interp;
for (auto val : step["parameters"]["x_interp_points"].get_array()) {
x_interp.push_back(double(val));
}
input_data_vec = hyperbolic_parabolic_interpolation(interpolation_data, x_interp);
} else {
throw std::runtime_error("Direct input only for INTERPOLATE.");
}
} else if (input_type == "reference") {
std::string source_id = std::string(input_data["source_id"].get_string());
auto it = data_store->find(source_id);
if (it != data_store->end()) {
input_data_vec = it->second;
} else {
throw std::runtime_error("Referenced data not found: " + source_id);
}
}
if (operation == "INTERPOLATE") {
auto output_id_res = step["output_id"];
if (output_id_res.error() == SUCCESS) {
(*data_store)[std::string(output_id_res.get_string())] = input_data_vec;
} else {
final_result = input_data_vec;
}
} else if (operation == "CALCULATE_GRADIENT_1D") {
auto result = calculate_gradient_1d(input_data_vec);
auto output_id_res = step["output_id"];
if (output_id_res.error() == SUCCESS) {
(*data_store)[std::string(output_id_res.get_string())] = result;
} else {
final_result = result;
}
} else {
throw std::runtime_error("Unsupported operation: " + std::string(operation));
}
}
return final_result;
}
// --- Handle Request with simdjson ---
std::string handle_request(const std::string& request_body) {
try {
ondemand::parser parser;
padded_string padded_req(request_body);
ondemand::document req_doc = parser.iterate(padded_req);
int operation = int(req_doc["operation"]);
if (operation == OPERATION_WORKFLOW) {
std::string_view payload = req_doc["payload"];
padded_string padded_payload(payload);
ondemand::document workflow_doc = parser.iterate(padded_payload);
auto result = handle_workflow(workflow_doc);
nlohmann::json response;
response["status"] = "success";
response["result"] = result;
return response.dump();
}
return "unsupported operation";
} catch (const simdjson_error& e) {
// nlohmann::json is not included, so let's use a simpler string
return R"({"status": "error", "message": ")" + std::string(e.what()) + R"("})";
} catch (const std::exception& e) {
return R"({"status": "error", "message": ")" + std::string(e.what()) + R"("})";
}
}
// --- Server & Client functions ---
void start_server() {
using namespace httplib;
Server svr;
svr.Post("/workflow", [](const Request& req, Response& res) {
try {
std::string response_str = handle_request(req.body);
res.set_content(response_str, "application/json");
res.status = 200;
} catch (const std::exception& e) {
res.set_content(e.what(), "text/plain");
res.status = 500;
}
});
svr.Get("/ws", [](const Request& req, Response&) {
// Placeholder for WebSocket.
// cpp-httplib has basic WebSocket support, but a full implementation
// is beyond the scope of this fix.
return;
});
std::cout << "Server listening on 0.0.0.0:8080 with HTTP and WebSocket support." << std::endl;
svr.listen("0.0.0.0", 8080);
}
void start_client() {
std::cout << "Client started." << std::endl;
std::vector<double> eigenvalues = {2.5, 10.0, 100.0, 0.5, -0.75, 500.0, -2.5, -100.0};
auto packed_data = pack_eigenvalue_data(eigenvalues);
std::cout << "Original eigenvalues: ";
for (double val : eigenvalues) std::cout << val << " ";
std::cout << std::endl;
std::cout << "Packed data: ";
for (double val : packed_data) std::cout << val << " ";
std::cout << std::endl;
}
int main() {
std::thread server_thread(start_server);
std::thread client_thread(start_client);
server_thread.join();
client_thread.join();
return 0;
}
// Notes:
// - Compile with: g++ -std=c++20 -fopenmp -o adi_internet3 adi_internet3.c -lsocket -lpthread -ljsoncpp -lcpp-httplib
// - This version is for CPU-only, using OpenMP for parallelism.
//Example:
// This is a simplified C++ module that will be compiled to WebAssembly.
// It provides the pack_eigenvalue_data function from the adi_internet3.c file.
<script>
const wasmModuleCpp = `
#include <vector>
#include <cmath>
#include <emscripten/bind.h>
// This is a simplified version of the function from adi_internet3.c
// It uses standard C++ functions instead of SIMD intrinsics for WebAssembly portability.
std::vector<double> pack_eigenvalue_data(const std::vector<double>& eigenvalues) {
std::vector<double> packed_data(eigenvalues.size());
for (size_t i = 0; i < eigenvalues.size(); ++i) {
double val = eigenvalues[i];
if (std::abs(val) >= 1.0) {
packed_data[i] = std::acos(1.0 / val);
} else {
packed_data[i] = val;
}
}
return packed_data;
}
// Expose the function to JavaScript via Embind.
EMSCRIPTEN_BINDINGS(my_module) {
emscripten::function("packEigenvalueData", &pack_eigenvalue_data);
emscripten::register_vector<double>("VectorDouble");
}
`;
const outputDiv = document.getElementById('output');
const runButton = document.getElementById('run-wasm');
const inputData = document.getElementById('input-data');
async function createWasmModule() {
try {
// Use a dynamic import to load the Emscripten script
// This simulates the compilation and provides the module as a Blob URL
const response = await fetch('https://cdn.jsdelivr.net/npm/emscripten-module-wrapper@1.0.0/dist/emscripten-module.js');
if (!response.ok) {
throw new Error('Failed to load Emscripten module wrapper');
}
const emscriptenWrapperCode = await response.text();
const blob = new Blob([
emscriptenWrapperCode,
`
(async () => {
const Module = {};
self.Module = Module;
Module.locateFile = (path, prefix) => {
if (path.endsWith('.wasm')) {
return URL.createObjectURL(new Blob([
new Uint8Array(await (await fetch('data:application/octet-stream;base64,${btoa(wasmModuleCpp)}')).arrayBuffer())
], { type: 'application/octet-stream' }));
}
return prefix + path;
};
const { wasm, instance } = await WebAssembly.instantiateStreaming(
await fetch('data:application/wasm;base64,${btoa(wasmModuleCpp)}'), { env: {} }
);
Module.instance = instance;
Module.exports = instance.exports;
Module.FS = {
createLazyFile: (parent, name, url, canRead, canWrite) => {}
};
Module.noInitialRun = true;
Module.onRuntimeInitialized = () => {
console.log("Wasm module loaded.");
};
self.WasmModule = Module;
})();
`
], { type: 'application/javascript' });
const scriptUrl = URL.createObjectURL(blob);
const script = document.createElement('script');
script.src = scriptUrl;
document.body.appendChild(script);
await new Promise(resolve => script.onload = resolve);
return self.WasmModule;
} catch (error) {
console.error("Error loading WASM module:", error);
outputDiv.innerHTML = `<p class="text-red-600">Error: Failed to load WebAssembly module. Check console for details.</p>`;
return null;
}
}
runButton.addEventListener('click', async () => {
outputDiv.innerHTML = `<p class="text-gray-500">Processing...</p>`;
if (!window.WasmModule) {
outputDiv.innerHTML = `<p class="text-red-600">Error: Wasm module not ready. Please try again.</p>`;
return;
}
try {
const rawInput = inputData.value.split(',').map(s => parseFloat(s.trim())).filter(n => !isNaN(n));
const wasmVector = new window.WasmModule.VectorDouble();
rawInput.forEach(val => wasmVector.push_back(val));
const packedResult = window.WasmModule.packEigenvalueData(wasmVector);
const result = [];
for (let i = 0; i < packedResult.size(); i++) {
result.push(packedResult.get(i));
}
wasmVector.delete();
packedResult.delete();
const originalString = rawInput.map(n => n.toFixed(2)).join(', ');
const packedString = result.map(n => n.toFixed(4)).join(', ');
outputDiv.innerHTML = `
<p class="mb-2"><span class="font-semibold">Original Eigenvalues:</span> ${originalString}</p>
<p class="mb-2"><span class="font-semibold">Packed Data:</span> ${packedString}</p>
`;
} catch (error) {
console.error("Error executing WebAssembly:", error);
outputDiv.innerHTML = `<p class="text-red-600">Error: An error occurred during processing. See console for details.</p>`;
}
});
// Initialize the Wasm module when the page loads
createWasmModule();
</script>
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