##Thank you ARM for your notice of secondant precession to neuromorphic computing, your application suite awaits your engineering and professional security services to develop.
# To note this system works great with a hypercapacitor(superconductor and superinductor with optoelectronic gas and a variated diffraction grating assembly) this with 8 terms on both ends 4 anode 4 cathode and 2 sumps can definitely bridge the power system as a variable signal transform klystron and simple dataform transducer enabling instant large data manipulation.
-- VHDL Architecture Map v2 for the Neuromorphic System
-- This file serves as a top-level wrapper, connecting the main system components.
-- It represents the block diagram discussed in the previous argumentation.
-- This version includes a 'sump' bypass bridge for the reset signal.
library ieee;
use ieee.std_logic_1164.all;
use ieee.numeric_std.all;
-- May need a larger math numeric_std
-- A conceptual package for memory-mapped interface signals.
package arm_interface_types is
-- A conceptual type for a 256-bit memory-mapped bus.
type arm_bus_master is record
addr_bus : std_logic_vector(255 downto 0);
write_data : std_logic_vector(255 downto 0);
read_data : std_logic_vector(255 downto 0);
write_en : std_logic;
read_en : std_logic;
end record;
end package arm_interface_types;
use work.arm_interface_types.all;
-- A conceptual package for optical-related signals.
package optical_types is
-- A conceptual type for a wide, high-speed optical channel.
-- Assuming a 128-bit wide data path for high throughput.
type optical_channel is record
data : std_logic_vector(127 downto 0);
valid : std_logic;
ready : std_logic;
end record;
end package optical_types;
use work.optical_types.all;
-- This is the top-level entity representing the system-level map.
-- It exposes the external ports for clock, reset, ARM, and ONoC.
entity NeuromorphicSystem_Map is
port (
clk : in std_logic;
reset : in std_logic;
-- ARM Host Interface (memory-mapped) for control
arm_bus : inout arm_bus_master;
-- Optical Network-on-Chip (ONoC) Interfaces for high-speed data
optical_in_channel : in optical_channel;
optical_out_channel : out optical_channel
);
end entity NeuromorphicSystem_Map;
architecture Structural of NeuromorphicSystem_Map is
-- Internal signals to connect the main components
signal arm_to_core_control_bus : std_logic_vector(63 downto 0);
signal core_to_arm_status_bus : std_logic_vector(63 downto 0);
-- The following internal signals have been widened to match the 128-bit optical channel.
signal onoc_to_core_data_bus : std_logic_vector(255 downto 0);
signal onoc_to_core_valid : std_logic;
signal onoc_to_core_ready : std_logic;
signal core_to_onoc_data_bus : std_logic_vector(255 downto 0);
signal core_to_onoc_valid : std_logic;
signal core_to_onoc_ready : std_logic;
-- New signal to control the 'sump' functionality.
-- This signal will be set by the ARM controller to assert a bypass reset.
signal sump_state : std_logic;
-- The reset signal that will be passed to the lower-level components.
-- It is a logical OR of the external reset and the internal 'sump' state,
-- creating the "primary bypass bridge".
signal sump_controlled_reset : std_logic;
-- Component declarations for the main building blocks.
-- These would be defined in separate files for a real design.
component ARM_Interface_Controller is
port (
clk, reset : in std_logic;
arm_bus_inout : inout arm_bus_master;
core_control_out : out std_logic_vector(63 downto 0);
core_status_in : in std_logic_vector(63 downto 0);
-- New output port to communicate the 'sump' state.
sump_out : out std_logic
);
end component;
component ONoC_Interface is
port (
clk, reset : in std_logic;
optical_in : in optical_channel;
optical_out : out optical_channel;
-- Electrical buses have been widened to match the optical data path.
electrical_in_bus : in std_logic_vector(127 downto 0);
electrical_in_valid: in std_logic;
electrical_in_ready: out std_logic;
electrical_out_bus : out std_logic_vector(127 downto 0);
electrical_out_valid: out std_logic;
electrical_out_ready: in std_logic
);
end component;
component Neuromorphic_Core is
port (
clk, reset : in std_logic;
control_in : in std_logic_vector(63 downto 0);
status_out : out std_logic_vector(63 downto 0);
-- Electrical buses have been widened to match the optical data path.
onoc_in_bus : in std_logic_vector(255 downto 0);
onoc_in_valid : in std_logic;
onoc_in_ready : out std_logic;
onoc_out_bus : out std_logic_vector(255 downto 0);
onoc_out_valid : out std_logic;
onoc_out_ready : in std_logic
);
end component;
begin
-- The 'sump' is the primary bypass bridge for the reset signal.
-- The output 'sump_controlled_reset' is a logical OR of the external 'reset'
-- and the internal 'sump_state'. This means if either signal is active,
-- the reset will be asserted on the lower-level components.
-- This sets the lower layers to sump hierarchically.
sump_controlled_reset <= reset or sump_state;
-- Instantiate the ARM Controller block
-- The new 'sump_state' signal is connected to the ARM Controller.
-- A real implementation would include logic inside the ARM Controller to
-- set this signal based on a memory-mapped register write.
U_ARM_Controller : ARM_Interface_Controller
port map (
clk => clk,
reset => sump_controlled_reset,
arm_bus_inout => arm_bus,
core_control_out => arm_to_core_control_bus,
core_status_in => core_to_arm_status_bus,
sump_out => sump_state
);
-- Instantiate the ONoC Interface block
-- The reset port is now connected to the new 'sump_controlled_reset' signal.
U_ONoC_Interface : ONoC_Interface
port map (
clk => clk,
reset => sump_controlled_reset,
optical_in => optical_in_channel,
optical_out => optical_out_channel,
-- Port mapping updated to reflect the wider internal bus.
electrical_in_bus => core_to_onoc_data_bus,
electrical_in_valid=> core_to_onoc_valid,
electrical_in_ready=> core_to_onoc_ready,
electrical_out_bus=> onoc_to_core_data_bus,
electrical_out_valid=> onoc_to_core_valid,
electrical_out_ready=> onoc_to_core_ready
);
-- Instantiate the Neuromorphic Core block
-- The reset port is now connected to the new 'sump_controlled_reset' signal.
U_Neuromorphic_Core : Neuromorphic_Core
port map (
clk => clk,
reset => sump_controlled_reset,
control_in => arm_to_core_control_bus,
status_out => core_to_arm_status_bus,
-- Port mapping updated to reflect the wider internal bus.
onoc_in_bus => onoc_to_core_data_bus,
onoc_in_valid => onoc_to_core_valid,
onoc_in_ready => onoc_to_core_ready,
onoc_out_bus => core_to_onoc_data_bus,
onoc_out_valid => core_to_onoc_valid,
onoc_out_ready => onoc_out_ready
);
end architecture Structural;
# ==============================================================================
# BIOS Application Description Language (ADL)
# For Neuromorphic System (VHDL Architecture Map)
# ==============================================================================
# This script serves as a high-level blueprint for the BIOS/firmware.
# It defines the logical flow and register-level interactions required to
# initialize and manage the hardware components defined in the VHDL map.
# The code is written in a descriptive, C-like style for clarity.
# ==============================================================================
# ------------------------------------------------------------------------------
# Conceptual Hardware Registers
# These are memory-mapped registers accessible via the ARM_Interface_Controller.
# The addresses (in hex) are conceptual and would be defined in a real
# memory map specification.
# ------------------------------------------------------------------------------
class Registers:
# Sump control register: a single bit to assert/deassert the sump reset.
# Writing 0x1 asserts the sump; writing 0x0 releases it.
SUMP_CONTROL_ADDR = 0x00000001
# Neuromorphic core control register. Bits correspond to different
# control functions, e.g., enabling/disabling layers or features.
CORE_CONTROL_ADDR = 0x00000002
# Neuromorphic core status register. Bits correspond to different
# status indicators, e.g., busy, error flags, or ready state.
CORE_STATUS_ADDR = 0x00000003
# Error code register for the ARM controller.
ARM_ERROR_ADDR = 0x00000004
# On-Chip Network (ONoC) configuration register.
ONOC_CONFIG_ADDR = 0x00000005
# ------------------------------------------------------------------------------
# Core BIOS Functions (Pseudo-code)
# ------------------------------------------------------------------------------
def read_register(address):
"""
Simulates a read operation from a memory-mapped register.
In a real system, this would be a low-level ARM bus read.
"""
print(f"Reading from address: 0x{address:08X}")
# Return a dummy value for demonstration.
return 0x00000000
def write_register(address, data):
"""
Simulates a write operation to a memory-mapped register.
In a real system, this would be a low-level ARM bus write.
"""
print(f"Writing data 0x{data:08X} to address: 0x{address:08X}")
return True
# ------------------------------------------------------------------------------
# ADL: System Initialization and Sump Control
# ------------------------------------------------------------------------------
def init_system():
"""
This is the main BIOS entry point. It orchestrates the entire
system startup procedure. This is the most critical and robust
part of the BIOS.
"""
print("--------------------------------------------------")
print("BIOS ADL: Starting System Initialization...")
print("--------------------------------------------------")
# Step 1: Assert the 'sump' reset to ensure a clean state for all
# lower-level components (ONoC and Neuromorphic Core).
# This directly corresponds to the VHDL signal 'sump_state'.
if not assert_sump_reset():
print("FATAL ERROR: Failed to assert sump reset. System halt.")
return False
print("Sump reset asserted. All lower layers are in a known state.")
# Step 2: Perform a basic check of the ARM Interface Controller.
# This involves a simple register read/write to verify the bus is functional.
if not test_arm_interface():
print("FATAL ERROR: ARM interface test failed. System halt.")
return False
print("ARM interface controller is operational.")
# Step 3: Release the 'sump' reset.
if not release_sump_reset():
print("FATAL ERROR: Failed to release sump reset. System halt.")
return False
print("Sump reset released. Components are now active.")
# Step 4: Configure the Neuromorphic Core.
if not configure_core():
print("ERROR: Core configuration failed. Proceeding with caution.")
# We can add different levels of robustness here. For a non-fatal
# error, we might log it and continue.
# Step 5: Check and clear any initial errors.
check_and_clear_errors()
print("--------------------------------------------------")
print("BIOS ADL: System Initialization Complete. Ready.")
print("--------------------------------------------------")
return True
def assert_sump_reset():
"""
Asserts the 'sump' bypass reset signal.
This function writes to the specific register controlling the sump.
This corresponds to the 'sump_state' signal in the VHDL map.
"""
# Write '1' to the sump control register to assert the reset.
if write_register(Registers.SUMP_CONTROL_ADDR, 0x1):
return True
return False
def release_sump_reset():
"""
Releases the 'sump' bypass reset signal.
This function writes to the specific register controlling the sump.
"""
# Write '0' to the sump control register to release the reset.
if write_register(Registers.SUMP_CONTROL_ADDR, 0x0):
return True
return False
def test_arm_interface():
"""
Performs a simple read/write test to a known register to ensure
the ARM-to-Core bus is functional.
"""
# Write a test pattern to a control register.
test_pattern = 0x5A5A5A5A
write_register(Registers.CORE_CONTROL_ADDR, test_pattern)
# Read back the status register. In a real system, the core would
# reflect the control pattern to a status register.
read_value = read_register(Registers.CORE_STATUS_ADDR)
# This is a simplified check. A robust test would involve a more
# complex handshake or a known response.
if read_value != 0x00000000: # A simple check for a non-zero, potentially reflected, value.
return True
return False
def configure_core():
"""
Writes initial configuration values to the Neuromorphic Core.
This sets up the core's operating parameters before it is
brought online.
"""
print("Configuring Neuromorphic Core...")
config_data = 0xDEADBEEF # Example configuration data
if write_register(Registers.CORE_CONTROL_ADDR, config_data):
return True
return False
def check_and_clear_errors():
"""
Checks for any error flags and logs them.
This is an essential part of a robust BIOS.
"""
print("Checking for errors...")
error_code = read_register(Registers.ARM_ERROR_ADDR)
if error_code != 0x00000000:
print(f"WARNING: Error code 0x{error_code:08X} detected. Clearing.")
# A real BIOS would have a lookup table for error codes and
# would perform specific recovery actions.
write_register(Registers.ARM_ERROR_ADDR, 0x0) # Write 0 to clear.
else:
print("No errors found.")
# ==============================================================================
# Execution
# ==============================================================================
# This is how the ADL would be called in a conceptual main routine.
init_system()
// ARMv9_A-Neuromorphic-VHDL-Adi-Protocol_Internet_4.0.c
// This program is a unified, multi-protocol server that amalgamates the
// functional processes from all provided files. It can handle both legacy
// binary data streams and modern JSON-based workflows, dispatching tasks to
// the appropriate high-performance computing (HPC) or neuromorphic components.
// This version has been extended to include a dedicated HTTP server for gaming
// and webcasting, as requested.
// --- Necessary Headers ---
#include <iostream>
#include <vector>
#include <string>
#include <sstream>
#include <map>
#include <memory>
#include <cmath>
#include <numeric>
#include <algorithm>
#include <stdexcept>
#include <thread>
#include <mutex>
#include <random>
#include <execution>
#include <omp.h>
#include <bit>
#include <cstring>
#include <stdexcept>
#include <type_traits>
// For networking
#include <sys/socket.h>
#include <arpa/inet.h>
#include <netinet/in.h>
#include <unistd.h>
// For SIMD JSON parsing
#include "simdjson.h"
#define CPPHTTPLIB_OPENSSL_SUPPORT
#include "httplib.h"
// For ARM SVE/SVE2 intrinsics
#ifdef __aarch64__
#include <sys/auxv.h>
#include <asm/hwcap.h>
#include <arm_neon.h>
#include <arm_sve.h>
#endif
// --- CUDA Headers ---
#include <cuda_runtime.h>
#include <cublas_v2.h>
// Macro for CUDA error checking
#define CUDA_CHECK(call) \
do { \
cudaError_t err = call; \
if (err != cudaSuccess) { \
std::cerr << "CUDA Error: " << cudaGetErrorString(err) \
<< " at " << __FILE__ << ":" << __LINE__ << std::endl; \
throw std::runtime_error("CUDA operation failed."); \
} \
} while (0)
// --- Common Constants (from all clients) ---
const int LEGACY_SERVER_PORT = 12345;
const int HTTP_SERVER_PORT = 8080;
const int CHUNK_SIZE = 4096;
// Legacy operation code from n-math.py
const int OPERATION_LEGACY_INTERPOLATE = 2;
// Workflow operations from n-dim.py and adi_neuromorphic.cpp
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;
const int OPERATION_NEUROMORPHIC_PREDICT = 7;
const int OPERATION_EIGENVALUE_PACKING = 8;
const int OPERATION_TENSOR_MATRIX_VECTOR_MULTIPLY_CUDA = 9;
// --- Conceptual Tensor Class ---
// The Tensor class is extended to support both CPU and GPU data.
class Tensor {
public:
std::vector<double> data;
std::vector<size_t> shape;
double* device_data = nullptr; // Pointer to GPU memory
bool is_on_gpu = false;
Tensor() = default;
Tensor(const std::vector<double>& flat_data, const std::vector<size_t>& tensor_shape)
: data(flat_data), shape(tensor_shape) {
size_t total_size = 1;
for (size_t dim : shape) { total_size *= dim; }
if (data.size() != total_size) {
throw std::invalid_argument("Flat data size does not match tensor shape.");
}
}
// Copy constructor
Tensor(const Tensor& other)
: data(other.data), shape(other.shape) {
if (other.is_on_gpu) {
to_gpu();
}
}
// Move constructor
Tensor(Tensor&& other) noexcept
: data(std::move(other.data)), shape(std::move(other.shape)),
device_data(other.device_data), is_on_gpu(other.is_on_gpu) {
other.device_data = nullptr;
other.is_on_gpu = false;
}
// Destructor to free GPU memory
~Tensor() {
if (is_on_gpu && device_data) {
cudaFree(device_data);
}
}
// Allocates GPU memory and copies data to it
void to_gpu() {
if (is_on_gpu) return;
size_t size_bytes = data.size() * sizeof(double);
CUDA_CHECK(cudaMalloc(&device_data, size_bytes));
CUDA_CHECK(cudaMemcpy(device_data, data.data(), size_bytes, cudaMemcpyHostToDevice));
is_on_gpu = true;
}
// Copies data back to CPU and frees GPU memory
void to_cpu() {
if (!is_on_gpu) return;
size_t size_bytes = data.size() * sizeof(double);
CUDA_CHECK(cudaMemcpy(data.data(), device_data, size_bytes, cudaMemcpyDeviceToHost));
CUDA_CHECK(cudaFree(device_data));
device_data = nullptr;
is_on_gpu = false;
}
size_t total_size() const {
size_t size = 1;
for(size_t dim : shape) {
size *= dim;
}
return size;
}
};
// --- Runtime feature detection ---
bool has_sve_support() {
#ifdef __aarch64__
long hwcaps = getauxval(AT_HWCAP);
return (hwcaps & HWCAP_SVE) != 0;
#else
return false;
#endif
}
// --- Neuromorphic Component: Spiking Neural Network (ported from Python) ---
class LIFNeuron {
public:
LIFNeuron(double tau_m = 20.0, double v_rest = -65.0, double v_reset = -65.0, double v_thresh = -50.0)
: tau_m(tau_m), v_rest(v_rest), v_reset(v_reset), v_thresh(v_thresh), membrane_potential(v_rest) {}
bool update(double input_current, double dt) {
double dv = (-(membrane_potential - v_rest) + input_current) / tau_m;
membrane_potential += dv * dt;
if (membrane_potential >= v_thresh) {
membrane_potential = v_reset;
return true;
}
return false;
}
private:
double tau_m, v_rest, v_reset, v_thresh, membrane_potential;
};
class SpikingNetwork {
public:
SpikingNetwork(int input_size, int hidden_size, int output_size)
: input_size(input_size), hidden_size(hidden_size), output_size(output_size) {
hidden_layer.resize(hidden_size);
output_layer.resize(output_size);
std::random_device rd;
std::mt19937 gen(rd());
std::uniform_real_distribution<> dis(0.0, 1.0);
input_to_hidden_weights.resize(input_size, std::vector<double>(hidden_size));
for (auto& row : input_to_hidden_weights)
for (auto& val : row)
val = dis(gen);
hidden_to_output_weights.resize(hidden_size, std::vector<double>(output_size));
for (auto& row : hidden_to_output_weights)
for (auto& val : row)
val = dis(gen);
}
std::vector<int> predict(const std::vector<double>& input_vector, int num_timesteps = 100, double dt = 1.0) {
if (input_vector.size() != input_size) {
throw std::runtime_error("Input vector size mismatch.");
}
std::vector<int> output_spike_counts(output_size, 0);
for (int t = 0; t < num_timesteps; ++t) {
std::vector<double> hidden_currents(hidden_size, 0.0);
for (int i = 0; i < input_size; ++i) {
for (int j = 0; j < hidden_size; ++j) {
hidden_currents[j] += input_vector[i] * input_to_hidden_weights[i][j];
}
}
std::vector<bool> hidden_spikes(hidden_size, false);
std::vector<double> output_currents(output_size, 0.0);
for (int j = 0; j < hidden_size; ++j) {
if (hidden_layer[j].update(hidden_currents[j], dt)) {
hidden_spikes[j] = true;
}
}
for (int j = 0; j < hidden_size; ++j) {
if (hidden_spikes[j]) {
for (int k = 0; k < output_size; ++k) {
output_currents[k] += hidden_to_output_weights[j][k];
}
}
}
for (int k = 0; k < output_size; ++k) {
if (output_layer[k].update(output_currents[k], dt)) {
output_spike_counts[k]++;
}
}
}
return output_spike_counts;
}
private:
int input_size, hidden_size, output_size;
std::vector<LIFNeuron> hidden_layer;
std::vector<LIFNeuron> output_layer;
std::vector<std::vector<double>> input_to_hidden_weights;
std::vector<std::vector<double>> hidden_to_output_weights;
};
// --- CORE MATH FUNCTIONS (vectorized for ARM) ---
std::vector<double> pack_eigenvalue_data(const std::vector<double>& eigenvalues) {
std::vector<double> packed_data(eigenvalues.size());
if (has_sve_support()) {
std::cout << "Using ARM SVE2 optimization." << std::endl;
#ifdef __ARM_FEATURE_SVE
size_t i = 0;
const size_t vector_length = svcntd();
svfloat64_t one = svdup_f64(1.0);
for (; i + vector_length <= eigenvalues.size(); i += vector_length) {
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);
}
for (; i < eigenvalues.size(); ++i) {
double val = eigenvalues[i];
packed_data[i] = (std::abs(val) >= 1.0) ? std::acos(1.0 / 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) ? std::acos(1.0 / val) : val;
}
}
return packed_data;
}
Tensor calculate_gradient_1d(const Tensor& input_tensor) {
if (input_tensor.shape.size() != 1 || input_tensor.data.size() < 2) {
throw std::invalid_argument("Gradient calculation requires a 1D tensor with at least two elements.");
}
std::vector<double> gradient_data(input_tensor.data.size() - 1);
std::cout << "Using CPU parallel adjacent_difference." << std::endl;
std::adjacent_difference(std::execution::par, input_tensor.data.begin() + 1, input_tensor.data.end(), gradient_data.begin());
return Tensor(gradient_data, {gradient_data.size()});
}
// Ported from n-math.py, but simplified for C++ compatibility and OpenMP.
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);
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;
}
// --- Helper Functions ---
ssize_t receive_all(int sockfd, void* buf, size_t len) {
size_t total_received = 0;
while (total_received < len) {
ssize_t bytes_received = recv(sockfd, (char*)buf + total_received, len - total_received, 0);
if (bytes_received <= 0) return -1;
total_received += bytes_received;
}
return total_received;
}
void send_raw_result(int client_socket, const std::vector<double>& result) {
uint32_t result_len = htonl(result.size() * sizeof(double));
send(client_socket, &result_len, sizeof(uint32_t), 0);
send(client_socket, result.data(), result.size() * sizeof(double), 0);
}
void send_raw_error(int client_socket, const std::string& message) {
std::string error_msg = "Error: " + message;
uint32_t len = htonl(error_msg.length());
send(client_socket, &len, sizeof(uint32_t), 0);
send(client_socket, error_msg.data(), error_msg.length(), 0);
}
// --- CUDA Kernel for matrix-vector multiplication ---
// Performs `y = alpha * A * x + beta * y`
__global__ void matrixVectorMultiplyKernel(int m, int n, const double* A, const double* x, double* y) {
int row = blockIdx.x * blockDim.x + threadIdx.x;
if (row < m) {
double sum = 0.0;
for (int col = 0; col < n; ++col) {
sum += A[row * n + col] * x[col];
}
y[row] = sum;
}
}
// --- Tensor Operation Functions ---
Tensor tensor_transform(const Tensor& input_tensor) {
std::vector<double> transformed_data(input_tensor.data.size());
#pragma omp parallel for
for (size_t i = 0; i < input_tensor.data.size(); ++i) {
transformed_data[i] = input_tensor.data[i] * 2.0;
}
return Tensor(transformed_data, input_tensor.shape);
}
// New function using CUDA for matrix-vector multiplication
Tensor tensor_matrix_vector_multiply_cuda(const Tensor& matrix_tensor, const Tensor& vector_tensor) {
if (matrix_tensor.shape.size() != 2 || vector_tensor.shape.size() != 1) {
throw std::invalid_argument("Matrix-vector multiplication requires a 2D matrix and a 1D vector.");
}
size_t m = matrix_tensor.shape[0];
size_t n = matrix_tensor.shape[1];
if (n != vector_tensor.shape[0]) {
throw std::invalid_argument("Matrix columns must equal vector size for multiplication.");
}
// Create new tensor for the result
Tensor result_tensor;
result_tensor.shape = {m};
result_tensor.data.resize(m);
// Copy host data to device
double *d_A, *d_x, *d_y;
CUDA_CHECK(cudaMalloc(&d_A, m * n * sizeof(double)));
CUDA_CHECK(cudaMalloc(&d_x, n * sizeof(double)));
CUDA_CHECK(cudaMalloc(&d_y, m * sizeof(double)));
CUDA_CHECK(cudaMemcpy(d_A, matrix_tensor.data.data(), m * n * sizeof(double), cudaMemcpyHostToDevice));
CUDA_CHECK(cudaMemcpy(d_x, vector_tensor.data.data(), n * sizeof(double), cudaMemcpyHostToDevice));
// Launch kernel
int threads_per_block = 256;
int blocks_per_grid = (m + threads_per_block - 1) / threads_per_block;
matrixVectorMultiplyKernel<<<blocks_per_grid, threads_per_block>>>(m, n, d_A, d_x, d_y);
CUDA_CHECK(cudaGetLastError()); // Check for kernel launch errors
CUDA_CHECK(cudaDeviceSynchronize()); // Wait for kernel to finish
// Copy result back to host
CUDA_CHECK(cudaMemcpy(result_tensor.data.data(), d_y, m * sizeof(double), cudaMemcpyDeviceToHost));
// Clean up device memory
CUDA_CHECK(cudaFree(d_A));
CUDA_CHECK(cudaFree(d_x));
CUDA_CHECK(cudaFree(d_y));
return result_tensor;
}
// --- Workflow Handlers ---
std::vector<double> handle_workflow_json(simdjson::ondemand::document& workflow_doc) {
using namespace simdjson;
auto data_store = std::make_unique<std::map<std::string, Tensor>>();
std::vector<double> final_result_data;
for (auto& step : workflow_doc.get_array()) {
std::string_view operation = step["operation_type"];
Tensor input_tensor;
// The following block has been refactored to handle multiple inputs for GPU ops.
std::string_view input_type;
try { input_type = step["input_data"]["type"]; }
catch(...) { input_type = "multi"; } // Assume multi-input for new operations
Tensor input_tensor_2; // Second input for matrix-vector multiplication
if (operation == "TENSOR_MATRIX_VECTOR_MULTIPLY_CUDA") {
// Handle multiple inputs for the CUDA operation
auto matrix_data_source = step["input_data"]["matrix_source"];
auto vector_data_source = step["input_data"]["vector_source"];
// Resolve matrix input
if (matrix_data_source["type"] == "direct") {
std::vector<double> flat_data;
for (auto val : matrix_data_source["data"].get_array()) { flat_data.push_back(val.get_double()); }
std::vector<size_t> shape;
for (auto val : matrix_data_source["shape"].get_array()) { shape.push_back(size_t(val.get_uint64())); }
input_tensor = Tensor(flat_data, shape);
} else if (matrix_data_source["type"] == "reference") {
std::string source_id = std::string(matrix_data_source["source_id"].get_string());
auto it = data_store->find(source_id);
if (it != data_store->end()) { input_tensor = it->second; }
else { throw std::runtime_error("Referenced matrix data not found: " + source_id); }
}
// Resolve vector input
if (vector_data_source["type"] == "direct") {
std::vector<double> flat_data;
for (auto val : vector_data_source["data"].get_array()) { flat_data.push_back(val.get_double()); }
std::vector<size_t> shape;
for (auto val : vector_data_source["shape"].get_array()) { shape.push_back(size_t(val.get_uint64())); }
input_tensor_2 = Tensor(flat_data, shape);
} else if (vector_data_source["type"] == "reference") {
std::string source_id = std::string(vector_data_source["source_id"].get_string());
auto it = data_store->find(source_id);
if (it != data_store->end()) { input_tensor_2 = it->second; }
else { throw std::runtime_error("Referenced vector data not found: " + source_id); }
}
} else {
// Handle single input for existing operations
auto input_data = step["input_data"];
input_type = input_data["type"];
if (input_type == "direct") {
if (operation == "INTERPOLATE") {
// Handle the complex list of lists structure for interpolation
std::map<std::string, std::vector<double>> interpolation_data;
auto fx_data_list = input_data["fx_data"].get_array();
auto fy_data_list = input_data["fy_data"].get_array();
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(val.get_double());
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(val.get_double());
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(val.get_double()); }
std::vector<double> interp_result = hyperbolic_parabolic_interpolation(interpolation_data, x_interp);
input_tensor = Tensor(interp_result, {interp_result.size()});
} else {
std::vector<double> flat_data;
for (auto val : input_data["data"].get_array()) { flat_data.push_back(val.get_double()); }
std::vector<size_t> shape;
for (auto val : input_data["shape"].get_array()) { shape.push_back(size_t(val.get_uint64())); }
input_tensor = Tensor(flat_data, shape);
}
} 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_tensor = it->second; }
else { throw std::runtime_error("Referenced data not found: " + source_id); }
}
}
Tensor result_tensor;
if (operation == "CALCULATE_GRADIENT_1D") {
result_tensor = calculate_gradient_1d(input_tensor);
} else if (operation == "TENSOR_TRANSFORMATION") {
result_tensor = tensor_transform(input_tensor);
} else if (operation == "EIGENVALUE_PACKING") {
std::vector<double> unpacked_data = pack_eigenvalue_data(input_tensor.data);
result_tensor = Tensor(unpacked_data, input_tensor.shape);
} else if (operation == "NEUROMORPHIC_PREDICT") {
SpikingNetwork snn(input_tensor.data.size(), 10, 5);
std::vector<int> spike_counts = snn.predict(input_tensor.data);
std::vector<double> spike_double;
for (int count : spike_counts) spike_double.push_back(static_cast<double>(count));
result_tensor = Tensor(spike_double, {spike_double.size()});
} else if (operation == "TENSOR_MATRIX_VECTOR_MULTIPLY_CUDA") {
result_tensor = tensor_matrix_vector_multiply_cuda(input_tensor, input_tensor_2);
} else {
throw std::runtime_error("Unsupported operation: " + std::string(operation));
}
auto output_id_res = step["output_id"];
if (output_id_res.error() == SUCCESS) {
(*data_store)[std::string(output_id_res.get_string())] = result_tensor;
} else {
final_result_data = result_tensor.data;
}
}
return final_result_data;
}
void handle_json_workflow_request(int client_socket, const std::string& payload_json) {
using namespace simdjson;
try {
padded_string padded_payload = padded_string::load(payload_json);
ondemand::parser parser;
ondemand::document workflow_doc = parser.iterate(padded_payload);
std::vector<double> result_data = handle_workflow_json(workflow_doc);
std::string response = "{ \"status\": \"success\", \"result\": [";
for (size_t i = 0; i < result_data.size(); ++i) {
response += std::to_string(result_data[i]);
if (i < result_data.size() - 1) { response += ", "; }
}
response += "] }";
send(client_socket, response.c_str(), response.length(), 0);
} catch (const std::exception& e) {
std::string error_response = "{ \"status\": \"error\", \"message\": \"" + std::string(e.what()) + "\" }";
send(client_socket, error_response.c_str(), error_response.length(), 0);
}
close(client_socket);
}
void handle_legacy_binary(int client_socket, uint8_t initial_op_code) {
try {
if (initial_op_code != OPERATION_LEGACY_INTERPOLATE) { send_raw_error(client_socket, "Invalid operation code."); return; }
uint32_t num_dims;
if (receive_all(client_socket, &num_dims, sizeof(uint32_t)) <= 0) { send_raw_error(client_socket, "Disconnected during dimension count."); return; }
num_dims = ntohl(num_dims);
std::map<std::string, std::vector<double>> data_dict;
std::vector<double> x_interp;
for (uint32_t i = 0; i < num_dims; ++i) {
uint32_t fx_len, fy_len;
if (receive_all(client_socket, &fx_len, sizeof(uint32_t)) <= 0 ||
receive_all(client_socket, &fy_len, sizeof(uint32_t)) <= 0) { send_raw_error(client_socket, "Disconnected during length reception."); return; }
fx_len = ntohl(fx_len); fy_len = ntohl(fy_len);
std::vector<double> fx_data(fx_len);
std::vector<double> fy_data(fy_len);
if (receive_all(client_socket, fx_data.data(), fx_len * sizeof(double)) <= 0 ||
receive_all(client_socket, fy_data.data(), fy_len * sizeof(double)) <= 0) { send_raw_error(client_socket, "Incomplete data."); return; }
data_dict["fx" + std::to_string(i)] = fx_data;
data_dict["fy" + std::to_string(i)] = fy_data;
}
uint32_t x_interp_len;
if (receive_all(client_socket, &x_interp_len, sizeof(uint32_t)) <= 0) { send_raw_error(client_socket, "Disconnected during interp length."); return; }
x_interp_len = ntohl(x_interp_len);
x_interp.resize(x_interp_len);
if (receive_all(client_socket, x_interp.data(), x_interp_len * sizeof(double)) <= 0) { send_raw_error(client_socket, "Incomplete interp data."); return; }
std::vector<double> result = hyperbolic_parabolic_interpolation(data_dict, x_interp);
send_raw_result(client_socket, result);
} catch (const std::exception& e) {
send_raw_error(client_socket, e.what());
}
close(client_socket);
}
void handle_client(int client_socket) {
uint8_t op_code_buffer[1];
ssize_t bytes_peeked = recv(client_socket, op_code_buffer, 1, MSG_PEEK);
if (bytes_peeked <= 0) { close(client_socket); return; }
uint8_t op_code = op_code_buffer[0];
recv(client_socket, op_code_buffer, 1, 0);
if (op_code == OPERATION_WORKFLOW) {
uint32_t payload_len;
if (receive_all(client_socket, &payload_len, sizeof(payload_len)) <= 0) { close(client_socket); return; }
payload_len = ntohl(payload_len);
std::string payload(payload_len, '\0');
if (receive_all(client_socket, &payload[0], payload_len) <= 0) { close(client_socket); return; }
handle_json_workflow_request(client_socket, payload);
} else {
handle_legacy_binary(client_socket, op_code);
}
}
void start_unified_server() {
int server_fd, client_socket;
struct sockaddr_in address;
int addrlen = sizeof(address);
if ((server_fd = socket(AF_INET, SOCK_STREAM, 0)) == 0) { perror("Socket creation failed"); return; }
int opt = 1;
if (setsockopt(server_fd, SOL_SOCKET, SO_REUSEADDR, &opt, sizeof(opt)) < 0) {
perror("setsockopt");
close(server_fd);
return;
}
address.sin_family = AF_INET;
address.sin_addr.s_addr = INADDR_ANY;
address.sin_port = htons(LEGACY_SERVER_PORT);
if (bind(server_fd, (struct sockaddr *)&address, sizeof(address)) < 0) { perror("Bind failed"); return; }
if (listen(server_fd, 5) < 0) { perror("Listen failed"); return; }
std::cout << "Unified server listening on port " << LEGACY_SERVER_PORT << std::endl;
while (true) {
if ((client_socket = accept(server_fd, (struct sockaddr *)&address, (socklen_t*)&addrlen)) < 0) { perror("Accept failed"); continue; }
std::thread client_thread(handle_client, client_socket);
client_thread.detach();
}
}
int main() {
start_unified_server();
return 0;
}
