// Define a function for running the simulation of a population system S with timestep
// dt. The number of timesteps and the data buffer are parameters of the defined function.
static Func <int, double[, ], int> DefineSimulate(double dt, PopulationSystem S)
{
CodeGen code = new CodeGen();
// Define a parameter for the current population x, and define mappings to the
// expressions defined above.
LinqExpr N = code.Decl <int>(Scope.Parameter, "N");
LinqExpr Data = code.Decl <double[, ]>(Scope.Parameter, "Data");
// Loop over the sample range requested. Note that this loop is a 'runtime' loop,
// while the rest of the loops nested in the body of this loop are 'compile time' loops.
LinqExpr n = code.DeclInit <int>("n", 1);
code.For(
() => { },
LinqExpr.LessThan(n, N),
() => code.Add(LinqExpr.PostIncrementAssign(n)),
() =>
{
// Define expressions representing the population of each species.
List <Expression> x = new List <Expression>();
for (int i = 0; i < S.N; ++i)
{
// Define a variable xi.
Expression xi = "x" + i.ToString();
x.Add(xi);
// xi = Data[n, i].
code.DeclInit(xi, LinqExpr.ArrayAccess(Data, LinqExpr.Subtract(n, LinqExpr.Constant(1)), LinqExpr.Constant(i)));
}
for (int i = 0; i < S.N; ++i)
{
// This list is the elements of the sum representing the i'th
// row of f, i.e. r_i + (A*x)_i.
Expression dx_dt = 1;
for (int j = 0; j < S.N; ++j)
{
dx_dt -= S.A[i, j] * x[j];
}
// Define dx_i/dt = x_i * f_i(x), as per the Lotka-Volterra equations.
dx_dt *= x[i] * S.r[i];
// Euler's method for x(t) is: x(t) = x(t - h) + h * x'(t - h).
Expression integral = x[i] + dt * dx_dt;
// Data[n, i] = Data[n - 1, i] + dt * dx_dt;
code.Add(LinqExpr.Assign(
LinqExpr.ArrayAccess(Data, n, LinqExpr.Constant(i)),
code.Compile(integral)));
}
});
code.Return(N);
// Compile the generated code.
LinqExprs.Expression <Func <int, double[, ], int> > expr = code.Build <Func <int, double[, ], int> >();
return(expr.Compile());
}
// Run the simulation of the population system S for N timesteps of length dt.
// The population at each timestep is stored in the rows of Data.
static void SimulateNative(int N, double dt, double[,] Data, PopulationSystem S)
{
// Loop over the sample range requested.
for (int n = 1; n < N; ++n)
{
// Loop over the species and compute the population change.
for (int i = 0; i < S.N; ++i)
{
double dx_dt = 1.0;
for (int j = 0; j < S.N; ++j)
{
dx_dt -= S.A[i, j] * Data[n - 1, j];
}
dx_dt *= S.r[i] * Data[n - 1, i];
Data[n, i] = Data[n - 1, i] + dt * dx_dt;
}
}
}
static void Main(string[] args)
{
// The number of timesteps simulated.
const int N = 100000;
// Time between timesteps.
const double dt = 0.001;
// First, we define an example population system description:
PopulationSystem S = new PopulationSystem(4);
S.x[0] = 0.2;
S.x[1] = 0.4586;
S.x[2] = 0.1307;
S.x[3] = 0.3557;
S.r[0] = 1;
S.r[1] = 0.72;
S.r[2] = 1.53;
S.r[3] = 1.27;
S.A[0, 0] = 1; S.A[0, 1] = 1.09; S.A[0, 2] = 1.52; S.A[0, 3] = 0;
S.A[1, 0] = 0; S.A[1, 1] = 1; S.A[1, 2] = 0.44; S.A[1, 3] = 1.36;
S.A[2, 0] = 2.33; S.A[2, 1] = 0; S.A[2, 2] = 1; S.A[2, 3] = 0.47;
S.A[3, 0] = 1.21; S.A[3, 1] = 0.51; S.A[3, 2] = 0.35; S.A[3, 3] = 1;
// Define a function to run the simulation specifically for S.
Func <int, double[, ], int> SimulateAlgebra = DefineSimulate(dt, S);
double native = RunSimulation((_N, _dt, _Data) => SimulateNative(_N, _dt, _Data, S), N, dt, S, "Native Simulation");
double hardcoded = RunSimulation((_N, _dt, _Data) => SimulateNativeHardCoded(_N, _Data), N, dt, S, "Native Simulation (hard-coded)");
double algebra = RunSimulation((_N, _dt, _Data) => SimulateAlgebra(_N, _Data), N, dt, S, "Algebraic Simulation");
Console.WriteLine("Algebraic simulation is {0:G3}x faster than native simulation, {1:G3}x faster than hardcoded simulation", native / algebra, hardcoded / algebra);
// On my machine, the above indicates that the algebraic simulation is 14x faster than native, and ~1.5x faster than the hardcoded simulation.
}
static double RunSimulation(Action <int, double, double[, ]> Simulate, int N, double dt, PopulationSystem S, string Title)
{
// Array of doubles representing the populations over time.
double[,] data = new double[N, S.N];
// Initialize the system to have the population specified in the system.
for (int i = 0; i < S.N; ++i)
{
data[0, i] = S.x[i];
}
// Run the simulation once first to ensure JIT has ocurred if necessary.
Simulate(N, dt, data);
// Start time of the simulation.
long start = Timer.Counter;
// run the simulation 100 times to avoid noise.
for (int i = 0; i < 100; ++i)
{
Simulate(N, dt, data);
}
double time = Timer.Delta(start);
Console.WriteLine("{0} time: {1}", Title, time);
// Plot the resulting data.
Plot plot = new Plot()
{
x0 = 0.0,
x1 = N,
Title = Title,
xLabel = "Timestep",
yLabel = "Population",
};
for (int i = 0; i < S.N; ++i)
{
KeyValuePair <double, double>[] points = new KeyValuePair <double, double> [N];
for (int j = 0; j < N; ++j)
{
points[j] = new KeyValuePair <double, double>(j, data[j, i]);
}
plot.Series.Add(new Scatter(points)
{
Name = i.ToString()
});
}
return(time);
}
// Run the simulation of the population system S for N timesteps of length dt.
// The population at each timestep is stored in the rows of Data.
static void SimulateNative(int N, double dt, double[,] Data, PopulationSystem S)
{
// Loop over the sample range requested.
for (int n = 1; n < N; ++n)
{
// Loop over the species and compute the population change.
for (int i = 0; i < S.N; ++i)
{
double dx_dt = 1.0;
for (int j = 0; j < S.N; ++j)
dx_dt -= S.A[i, j] * Data[n - 1, j];
dx_dt *= S.r[i] * Data[n - 1, i];
Data[n, i] = Data[n - 1, i] + dt * dx_dt;
}
}
}
static double RunSimulation(Action<int, double, double[,]> Simulate, int N, double dt, PopulationSystem S, string Title)
{
// Array of doubles representing the populations over time.
double[,] data = new double[N, S.N];
// Initialize the system to have the population specified in the system.
for (int i = 0; i < S.N; ++i)
data[0, i] = S.x[i];
// Run the simulation once first to ensure JIT has ocurred if necessary.
Simulate(N, dt, data);
// Start time of the simulation.
long start = Timer.Counter;
// run the simulation 100 times to avoid noise.
for (int i = 0; i < 100; ++i)
Simulate(N, dt, data);
double time = Timer.Delta(start);
Console.WriteLine("{0} time: {1}", Title, time);
// Plot the resulting data.
Plot plot = new Plot()
{
x0 = 0.0,
x1 = N,
Title = Title,
xLabel = "Timestep",
yLabel = "Population",
};
for (int i = 0; i < S.N; ++i)
{
KeyValuePair<double, double>[] points = new KeyValuePair<double, double>[N];
for (int j = 0; j < N; ++j)
points[j] = new KeyValuePair<double, double>(j, data[j, i]);
plot.Series.Add(new Scatter(points) { Name = i.ToString() });
}
return time;
}
static void Main(string[] args)
{
// The number of timesteps simulated.
const int N = 100000;
// Time between timesteps.
const double dt = 0.001;
// First, we define an example population system description:
PopulationSystem S = new PopulationSystem(4);
S.x[0] = 0.2;
S.x[1] = 0.4586;
S.x[2] = 0.1307;
S.x[3] = 0.3557;
S.r[0] = 1;
S.r[1] = 0.72;
S.r[2] = 1.53;
S.r[3] = 1.27;
S.A[0, 0] = 1; S.A[0, 1] = 1.09; S.A[0, 2] = 1.52; S.A[0, 3] = 0;
S.A[1, 0] = 0; S.A[1, 1] = 1; S.A[1, 2] = 0.44; S.A[1, 3] = 1.36;
S.A[2, 0] = 2.33; S.A[2, 1] = 0; S.A[2, 2] = 1; S.A[2, 3] = 0.47;
S.A[3, 0] = 1.21; S.A[3, 1] = 0.51; S.A[3, 2] = 0.35; S.A[3, 3] = 1;
// Define a function to run the simulation specifically for S.
Func<int, double[,], int> SimulateAlgebra = DefineSimulate(dt, S);
double native = RunSimulation((_N, _dt, _Data) => SimulateNative(_N, _dt, _Data, S), N, dt, S, "Native Simulation");
double hardcoded = RunSimulation((_N, _dt, _Data) => SimulateNativeHardCoded(_N, _Data), N, dt, S, "Native Simulation (hard-coded)");
double algebra = RunSimulation((_N, _dt, _Data) => SimulateAlgebra(_N, _Data), N, dt, S, "Algebraic Simulation");
Console.WriteLine("Algebraic simulation is {0:G3}x faster than native simulation, {1:G3}x faster than hardcoded simulation", native / algebra, hardcoded / algebra);
// On my machine, the above indicates that the algebraic simulation is 14x faster than native, and ~1.5x faster than the hardcoded simulation.
}
// Define a function for running the simulation of a population system S with timestep
// dt. The number of timesteps and the data buffer are parameters of the defined function.
static Func<int, double[, ], int> DefineSimulate(double dt, PopulationSystem S)
{
CodeGen code = new CodeGen();
// Define a parameter for the current population x, and define mappings to the
// expressions defined above.
LinqExpr N = code.Decl<int>(Scope.Parameter, "N");
LinqExpr Data = code.Decl<double[,]>(Scope.Parameter, "Data");
// Loop over the sample range requested. Note that this loop is a 'runtime' loop,
// while the rest of the loops nested in the body of this loop are 'compile time' loops.
LinqExpr n = code.DeclInit<int>("n", 1);
code.For(
() => { },
LinqExpr.LessThan(n, N),
() => code.Add(LinqExpr.PostIncrementAssign(n)),
() =>
{
// Define expressions representing the population of each species.
List<Expression> x = new List<Expression>();
for (int i = 0; i < S.N; ++i)
{
// Define a variable xi.
Expression xi = "x" + i.ToString();
x.Add(xi);
// xi = Data[n, i].
code.DeclInit(xi, LinqExpr.ArrayAccess(Data, LinqExpr.Subtract(n, LinqExpr.Constant(1)), LinqExpr.Constant(i)));
}
for (int i = 0; i < S.N; ++i)
{
// This list is the elements of the sum representing the i'th
// row of f, i.e. r_i + (A*x)_i.
Expression dx_dt = 1;
for (int j = 0; j < S.N; ++j)
dx_dt -= S.A[i, j] * x[j];
// Define dx_i/dt = x_i * f_i(x), as per the Lotka-Volterra equations.
dx_dt *= x[i] * S.r[i];
// Euler's method for x(t) is: x(t) = x(t - h) + h * x'(t - h).
Expression integral = x[i] + dt * dx_dt;
// Data[n, i] = Data[n - 1, i] + dt * dx_dt;
code.Add(LinqExpr.Assign(
LinqExpr.ArrayAccess(Data, n, LinqExpr.Constant(i)),
code.Compile(integral)));
}
});
code.Return(N);
// Compile the generated code.
LinqExprs.Expression<Func<int, double[,], int>> expr = code.Build<Func<int, double[,], int>>();
return expr.Compile();
}
