// Conceptual ARM Operand Set Translation
// This Operand codeset is a conceptual example and is not intended to be runnable on its own.
// It assumes a standard ARMv7-A architecture with VFPv4 (Vector Floating-Point) extensions, common for chipsets like the MT8196.
// C++ Function for Reference:
/*
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] = calculate_arcsecant(val);
} else {
packed_data[i] = val;
}
}
return packed_data;
}
*/
// --- ARM Operand Script ---
.global pack_eigenvalue_data
.type pack_eigenvalue_data, %function
// The function signature in ARM (following a common ABI):
// r0: Pointer to the input vector `eigenvalues` (const std::vector<double>&)
// r1: Size of the input vector
// r2: Pointer to the output vector `packed_data`
pack_eigenvalue_data:
// Standard function prologue: push link register (lr) and frame pointer (fp)
push {r4, r5, r6, lr}
// Set up local variables/pointers
mov r4, r0 // r4 = pointer to eigenvalues (input)
mov r5, r2 // r5 = pointer to packed_data (output)
mov r6, #0 // r6 = loop counter `i`
loop_start:
cmp r6, r1 // Compare loop counter `i` with vector size
bge loop_end // Branch if greater than or equal to (end of loop)
// Calculate memory address for the current `val` in the input vector
// LDRD loads a double-precision floating-point value (8 bytes)
// ldrd d0, [r4, r6, lsl #3] (Equivalent to: load d0, [r4 + r6*8])
// The `lsl #3` scales the index `r6` by 8 to get the byte offset for a double.
ldr d0, [r4, r6, lsl #3] // Load `val` into floating-point register d0
// Compare abs(val) with 1.0
// VABS.F64 d1, d0 // d1 = abs(val)
vabs.f64 d1, d0
// VCMPE.F64 d1, d1 // The C++ `std::abs` function is translated here
vcmp.f64 d1, #1.0 // Compare d1 with 1.0
vmrs APSR_nzcv, fpscr // Move floating-point status flags to ARM status register
// Branch based on the comparison result
bge .abs_greater_equal_1 // Branch if abs(val) >= 1.0
// `else` block: `packed_data[i] = val;`
// VSTR.F64 d0, [r5, r6, lsl #3] // Store the original value to the output vector
strd d0, [r5, r6, lsl #3]
b .loop_continue
.abs_greater_equal_1:
// `if` block: `packed_data[i] = calculate_arcsecant(val);`
// We would call a separate function here.
// In a real scenario, this would be a function call that takes a double
// in d0 and returns the result in d0.
// bl calculate_arcsecant_func
// For this conceptual example, let's just simulate a store
// This is where you would introduce a breakpoint for `break functionality` research
// to observe the input to the `calculate_arcsecant` function.
// VSTR.F64 d0, [r5, r6, lsl #3] // Store the calculated value to the output vector
strd d0, [r5, r6, lsl #3]
.loop_continue:
// Increment the loop counter
add r6, r6, #1
b loop_start // Jump back to the start of the loop
loop_end:
// Function epilogue: pop registers and return
pop {r4, r5, r6, pc}
//C++ translation for ARMv7-A
// Adi-Protocol_ARMv7A.c
#include <iostream>
#include <vector>
#include <string>
#include <map>
#include <memory>
#include <cmath>
#include <numeric> // For std::accumulate
#include <algorithm>
#include <stdexcept>
#include <thread>
#include <mutex>
// For JSON handling (requires nlohmann/json library)
#include "json.hpp"
using json = nlohmann::json;
// --- Mock Socket and Network Utilities for Conceptual Example ---
// Note: A real implementation would use a library like Boost.Asio or implement sockets manually.
// This is a simplified model to demonstrate the protocol logic.
class MockSocket {
public:
std::string buffer;
void sendall(const std::string& data) {
// Simulate sending
}
std::string recv(size_t size) {
// Simulate receiving
return "mock data"; // Return some dummy data for the example
}
};
std::string _recvall(MockSocket& sock, size_t n) {
std::string data;
while (data.size() < n) {
data += sock.recv(n - data.size());
}
return data;
}
// Helper to convert std::vector<double> to a mock binary string.
std::string _pack_data(const std::vector<double>& data) {
std::string binary_data(data.size() * sizeof(double), '\0');
std::memcpy(&binary_data[0], data.data(), binary_data.size());
return binary_data;
}
// Helper to convert a mock binary string to std::vector<double>.
std::vector<double> _unpack_data(const std::string& binary_data) {
std::vector<double> data(binary_data.size() / sizeof(double));
std::memcpy(data.data(), binary_data.data(), binary_data.size());
return data;
}
// --- Common Constants (Matches Python protocol) ---
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; // NEW: For relational compositions
// --- Helper Functions ---
double calculate_arcsecant(double val) {
if (std::abs(val) < 1.0) {
return NAN;
}
return std::acos(1.0 / val);
}
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] = calculate_arcsecant(val);
} else {
packed_data[i] = val;
}
}
return packed_data;
}
// --- FPU Operations (from Python adi_server.py) ---
std::vector<double> hyperbolic_parabolic_interpolation(
const std::map<std::string, std::vector<double>>& data_dict,
const std::vector<double>& x_interp) {
// ... (rest of the function, no changes needed for this refit)
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;
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.");
}
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;
double x2 = points[1].second;
double 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);
double y2 = find_y(x2);
double y3 = find_y(x3);
double L1 = ((x - x2) * (x - x3)) / ((x1 - x2) * (x1 - x3));
double L2 = ((x - x1) * (x - x3)) / ((x2 - x1) * (x2 - x3));
double L3 = ((x - x1) * (x - x2)) / ((x3 - x1) * (x3 - x2));
double interp_y = L1 * y1 + L2 * y2 + L3 * y3;
all_interp_y.push_back(interp_y);
}
}
return all_interp_y;
}
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;
for (size_t i = 0; i < data.size() - 1; ++i) {
gradient.push_back(data[i+1] - data[i]);
}
return gradient;
}
std::vector<double> handle_eigenvalue_reference_op(const std::vector<double>& packed_data) {
double sum = std::accumulate(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;
for (int i = 0; i < 5; ++i) {
result.push_back(mean_value * (i + 1));
}
return result;
}
// --- NEW: Workflow Handler from adi-ndim.py ---
std::vector<double> handle_workflow(const json& workflow) {
std::map<std::string, std::vector<double>> data_store;
std::vector<double> final_result;
for (const auto& step : workflow) {
std::string operation = step["operation_type"];
std::vector<double> input_data_vec;
// Determine input data source (direct or reference)
if (step["input_data"]["type"] == "direct") {
// Unpack direct input data
if (operation == "INTERPOLATE") {
const auto& fx_data_list = step["input_data"]["fx_data"];
const auto& fy_data_list = step["input_data"]["fy_data"];
const auto& fz_data_list = step["input_data"]["fz_data"];
std::map<std::string, std::vector<double>> interpolation_data;
for (size_t i = 0; i < fx_data_list.size(); ++i) {
interpolation_data["fx" + std::to_string(i)] = fx_data_list[i].get<std::vector<double>>();
interpolation_data["fy" + std::to_string(i)] = fy_data_list[i].get<std::vector<double>>();
// Note: fz_data would be handled here too if needed
}
std::vector<double> x_interp = step["parameters"]["x_interp_points"].get<std::vector<double>>();
input_data_vec = hyperbolic_parabolic_interpolation(interpolation_data, x_interp);
} else {
throw std::runtime_error("Direct input is only supported for INTERPOLATE operation in this mock.");
}
} else if (step["input_data"]["type"] == "reference") {
std::string source_id = step["input_data"]["source_id"];
if (data_store.find(source_id) != data_store.end()) {
input_data_vec = data_store[source_id];
} else {
throw std::runtime_error("Referenced data not found in store: " + source_id);
}
}
// Execute the operation
if (operation == "INTERPOLATE") {
// Result is already in input_data_vec from the direct path above
// Now, store it for future steps
if (step.count("output_id")) {
data_store[step["output_id"]] = input_data_vec;
} else {
final_result = input_data_vec;
}
} else if (operation == "CALCULATE_GRADIENT_1D") {
std::vector<double> result = calculate_gradient_1d(input_data_vec);
if (step.count("output_id")) {
data_store[step["output_id"]] = result;
} else {
final_result = result;
}
} else {
throw std::runtime_error("Unsupported operation type in workflow: " + operation);
}
}
return final_result;
}
// A simplified function to handle client requests.
std::string handle_request(const json& header, const std::string& binary_payload) {
if (header["operation"] == OPERATION_WORKFLOW) {
try {
// Mock unpacking of the binary payload into a vector
json workflow = json::parse(binary_payload);
std::vector<double> result = handle_workflow(workflow);
json response_header;
response_header["status"] = "success";
response_header["result"] = result;
return response_header.dump();
} catch (const std::exception& e) {
json error_header;
error_header["status"] = "error";
error_header["message"] = e.what();
return error_header.dump();
}
}
// ... (rest of the handle_request function, no changes needed)
return "unsupported operation";
}
// --- Main Server Functionality ---
void start_server() {
std::cout << "Server listening..." << std::endl;
MockSocket client_socket;
// Simulate a client request with the new workflow
std::cout << "Simulating a client request with a workflow..." << std::endl;
json workflow_mock = json::parse(R"(
[
{
"operation_type": "INTERPOLATE",
"input_data": {
"type": "direct",
"fx_data": [[1.0, 2.0, 3.0, 4.0, 5.0]],
"fy_data": [[10.0, 12.0, 15.0, 19.0, 25.0]],
"fz_data": [[20.0, 21.0, 23.0, 26.0, 30.0]]
},
"parameters": {
"x_interp_points": [1.5, 2.5, 3.5, 4.5]
},
"output_id": "interpolated_data"
},
{
"operation_type": "CALCULATE_GRADIENT_1D",
"input_data": {
"type": "reference",
"source_id": "interpolated_data"
}
}
]
)");
std::string workflow_payload = workflow_mock.dump();
json request_header;
request_header["operation"] = OPERATION_WORKFLOW;
std::string response = handle_request(request_header, workflow_payload);
std::cout << "Server response: " << response << std::endl;
}
// --- Main Client Functionality ---
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 (arcsecant and linear): ";
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;
}
// --- ARM Operand Set for ARMv9 utilizing SVE ---
.global pack_eigenvalue_data
.type pack_eigenvalue_data, %function
// The function signature in ARM (following a common ABI):
// x0: Pointer to the input vector `eigenvalues`
// x1: Size of the input vector
// x2: Pointer to the output vector `packed_data`
pack_eigenvalue_data:
// Standard function prologue: save used registers
stp x29, x30, [sp, #-16]! // Save frame pointer (x29) and link register (x30)
mov x29, sp // Set up new frame pointer
// Set up local variables/pointers
mov x4, x0 // x4 = pointer to eigenvalues (input)
mov x5, x2 // x5 = pointer to packed_data (output)
mov x6, #0 // x6 = loop counter `i`
// --- SVE Vectorized Loop ---
// SVE uses a loop predicate to handle different vector lengths
// and process data in chunks.
// Set up a vector of 1.0s for comparison.
mov d0, #1.0
fmov z0.d, d0 // Broadcast 1.0 to all elements of vector register z0
sve_loop_start:
// Determine the number of elements to process in this iteration.
whilelt p0.d, x6, x1 // p0.d is a predicate (boolean mask) for double elements.
// `whilelt` sets the predicate for elements where x6 < x1.
// If no elements remain, break the loop.
b.none sve_loop_end // Branch if none of the predicate lanes are true.
// Load a vector of double-precision floating-point values from memory.
// The predicate `p0` ensures we don't load past the end of the array.
ld1d z1.d, p0/z, [x4, x6, lsl #3] // Load a vector of doubles from `eigenvalues`
// The lsl #3 scales the index x6 by 8 for double-byte offsets.
// Compare abs(val) with 1.0
// First, compute the absolute value for each element.
fabs z2.d, z1.d // z2 = fabs(z1)
// Predicated comparison: generate a boolean predicate (mask) where abs(val) >= 1.0.
fcmge p1.d, p0/z, z2.d, z0.d // Compare z2 with z0, result is in predicate p1.d
// Calculate arcsecant for elements where the predicate is true.
// The `p1/m` indicates a merging predication.
fdiv z3.d, p1/m, z0.d, z1.d // z3 = 1.0 / z1 (only for active lanes in p1)
frecps z3.d, z3.d, z3.d // Reciprocal estimate (often part of a sequence for full precision)
frecpe z3.d, z3.d, z3.d
// A more realistic scenario would call a vectorized `calculate_arcsecant` function.
// This is a conceptual example. For an accurate arccos, we would use a library function
// or a hardware instruction if available.
// For this example, let's assume we have a vectorized arcsecant function.
// fsub z4.d, z3.d, z1.d // Placeholder for complex vectorized function
// Select between `arcsecant` and the original value based on the predicate.
sel z1.d, p1.d, z1.d, z3.d // Select `z3` if `p1` is true, otherwise `z1`.
// Store the results back to memory.
st1d z1.d, p0/z, [x5, x6, lsl #3] // Store the result vector to `packed_data`
// Increment the loop counter by the number of elements processed.
incd x6, all, x6 // Increment loop counter by SVE vector length
b sve_loop_start // Jump back to the start of the loop
sve_loop_end:
// Function epilogue: restore registers and return
ldp x29, x30, [sp], #16 // Restore frame pointer and link register
ret // Return from function
// ARMv9 Implementation of Adi Protocol (N-Dimensional and Eigenvalue Packing Included! :-S)
// Adi-Protocol_ARMv9.c
#include <iostream>
#include <vector>
#include <string>
#include <map>
#include <memory>
#include <cmath>
#include <numeric> // For std::accumulate
#include <algorithm>
#include <stdexcept>
#include <thread>
#include <mutex>
// For JSON handling (requires nlohmann/json library)
#include "json.hpp"
using json = nlohmann::json;
// --- Mock Socket and Network Utilities for Conceptual Example ---
// Note: A real implementation would use a library like Boost.Asio or implement sockets manually.
// This is a simplified model to demonstrate the protocol logic.
class MockSocket {
public:
std::string buffer;
void sendall(const std::string& data) {
// Simulate sending
}
std::string recv(size_t size) {
// Simulate receiving
return "mock data"; // Return some dummy data for the example
}
};
std::string _recvall(MockSocket& sock, size_t n) {
std::string data;
while (data.size() < n) {
data += sock.recv(n - data.size());
}
return data;
}
// Helper to convert std::vector<double> to a mock binary string.
std::string _pack_data(const std::vector<double>& data) {
std::string binary_data(data.size() * sizeof(double), '\0');
std::memcpy(&binary_data[0], data.data(), binary_data.size());
return binary_data;
}
// Helper to convert a mock binary string to std::vector<double>.
std::vector<double> _unpack_data(const std::string& binary_data) {
std::vector<double> data(binary_data.size() / sizeof(double));
std::memcpy(data.data(), binary_data.data(), binary_data.size());
return data;
}
// --- Common Constants (Matches Python protocol) ---
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; // NEW: For relational compositions
// --- Helper Functions ---
double calculate_arcsecant(double val) {
if (std::abs(val) < 1.0) {
return NAN;
}
return std::acos(1.0 / val);
}
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] = calculate_arcsecant(val);
} else {
packed_data[i] = val;
}
}
return packed_data;
}
// --- FPU Operations (from Python adi_server.py) ---
std::vector<double> hyperbolic_parabolic_interpolation(
const std::map<std::string, std::vector<double>>& data_dict,
const std::vector<double>& x_interp) {
// ... (rest of the function, no changes needed for this refit)
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;
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.");
}
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;
double x2 = points[1].second;
double 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);
double y2 = find_y(x2);
double y3 = find_y(x3);
double L1 = ((x - x2) * (x - x3)) / ((x1 - x2) * (x1 - x3));
double L2 = ((x - x1) * (x - x3)) / ((x2 - x1) * (x2 - x3));
double L3 = ((x - x1) * (x - x2)) / ((x3 - x1) * (x3 - x2));
double interp_y = L1 * y1 + L2 * y2 + L3 * y3;
all_interp_y.push_back(interp_y);
}
}
return all_interp_y;
}
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;
for (size_t i = 0; i < data.size() - 1; ++i) {
gradient.push_back(data[i+1] - data[i]);
}
return gradient;
}
std::vector<double> handle_eigenvalue_reference_op(const std::vector<double>& packed_data) {
double sum = std::accumulate(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;
for (int i = 0; i < 5; ++i) {
result.push_back(mean_value * (i + 1));
}
return result;
}
// --- NEW: Workflow Handler from adi-ndim.py ---
std::vector<double> handle_workflow(const json& workflow) {
std::map<std::string, std::vector<double>> data_store;
std::vector<double> final_result;
for (const auto& step : workflow) {
std::string operation = step["operation_type"];
std::vector<double> input_data_vec;
// Determine input data source (direct or reference)
if (step["input_data"]["type"] == "direct") {
// Unpack direct input data
if (operation == "INTERPOLATE") {
const auto& fx_data_list = step["input_data"]["fx_data"];
const auto& fy_data_list = step["input_data"]["fy_data"];
const auto& fz_data_list = step["input_data"]["fz_data"];
std::map<std::string, std::vector<double>> interpolation_data;
for (size_t i = 0; i < fx_data_list.size(); ++i) {
interpolation_data["fx" + std::to_string(i)] = fx_data_list[i].get<std::vector<double>>();
interpolation_data["fy" + std::to_string(i)] = fy_data_list[i].get<std::vector<double>>();
// Note: fz_data would be handled here too if needed
}
std::vector<double> x_interp = step["parameters"]["x_interp_points"].get<std::vector<double>>();
input_data_vec = hyperbolic_parabolic_interpolation(interpolation_data, x_interp);
} else {
throw std::runtime_error("Direct input is only supported for INTERPOLATE operation in this mock.");
}
} else if (step["input_data"]["type"] == "reference") {
std::string source_id = step["input_data"]["source_id"];
if (data_store.find(source_id) != data_store.end()) {
input_data_vec = data_store[source_id];
} else {
throw std::runtime_error("Referenced data not found in store: " + source_id);
}
}
// Execute the operation
if (operation == "INTERPOLATE") {
// Result is already in input_data_vec from the direct path above
// Now, store it for future steps
if (step.count("output_id")) {
data_store[step["output_id"]] = input_data_vec;
} else {
final_result = input_data_vec;
}
} else if (operation == "CALCULATE_GRADIENT_1D") {
std::vector<double> result = calculate_gradient_1d(input_data_vec);
if (step.count("output_id")) {
data_store[step["output_id"]] = result;
} else {
final_result = result;
}
} else {
throw std::runtime_error("Unsupported operation type in workflow: " + operation);
}
}
return final_result;
}
// A simplified function to handle client requests.
std::string handle_request(const json& header, const std::string& binary_payload) {
if (header["operation"] == OPERATION_WORKFLOW) {
try {
// Mock unpacking of the binary payload into a vector
json workflow = json::parse(binary_payload);
std::vector<double> result = handle_workflow(workflow);
json response_header;
response_header["status"] = "success";
response_header["result"] = result;
return response_header.dump();
} catch (const std::exception& e) {
json error_header;
error_header["status"] = "error";
error_header["message"] = e.what();
return error_header.dump();
}
}
// ... (rest of the handle_request function, no changes needed)
return "unsupported operation";
}
// --- Main Server Functionality ---
void start_server() {
std::cout << "Server listening..." << std::endl;
MockSocket client_socket;
// Simulate a client request with the new workflow
std::cout << "Simulating a client request with a workflow..." << std::endl;
json workflow_mock = json::parse(R"(
[
{
"operation_type": "INTERPOLATE",
"input_data": {
"type": "direct",
"fx_data": [[1.0, 2.0, 3.0, 4.0, 5.0]],
"fy_data": [[10.0, 12.0, 15.0, 19.0, 25.0]],
"fz_data": [[20.0, 21.0, 23.0, 26.0, 30.0]]
},
"parameters": {
"x_interp_points": [1.5, 2.5, 3.5, 4.5]
},
"output_id": "interpolated_data"
},
{
"operation_type": "CALCULATE_GRADIENT_1D",
"input_data": {
"type": "reference",
"source_id": "interpolated_data"
}
}
]
)");
std::string workflow_payload = workflow_mock.dump();
json request_header;
request_header["operation"] = OPERATION_WORKFLOW;
std::string response = handle_request(request_header, workflow_payload);
std::cout << "Server response: " << response << std::endl;
}
// --- Main Client Functionality ---
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 (arcsecant and linear): ";
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;
}
Thursday, August 21, 2025
ARMv7-A/9 Translation for Adi Protocol Suite! :-S*
Subscribe to:
Post Comments (Atom)
No comments:
Post a Comment