void LTSor::consumeInput() {
// create a tuple synch lock
tuple *synchLock = make_tuple("s", SYNCH_LOCK);
put_tuple(synchLock, &ctx);
// Initialise. note that as these can run in processes, the
// solution vector must live in tuple space to be communicated
// between the workers. Setting the solution variable is LOCAL ONLY
Matrix matrix = (Matrix) inputs[0];
Vector target = (Vector) inputs[1];
Vector solution = (Vector) outputs[0];
for (index_t r = 0; r < SOR_N; ++r) {
VECTOR(solution, r) = 1.0;
}
// put a tuple for the solution vector into tuple space
tuple *solutionTuple = make_tuple("ss", SOLUTION_VECTOR);
solutionTuple->elements[1].data.s.len = sizeof(solution);
solutionTuple->elements[1].data.s.ptr = (char*) solution;
// loop until we get the desired tolerance
real maxDiff = (real)(2 * SOR_TOLERANCE);
for (index_t t = 0; (t < SOR_MAX_ITERS) && (maxDiff >= SOR_TOLERANCE); t++) {
maxDiff = 0.0;
for (index_t r = 0; r < SOR_N; ++r) {
// compute sum
real sum = solutionSum(r);
// calculate new solution
real oldSolution = VECTOR(solution, r);
VECTOR(solution, r) = (real)(
(1.0 - SOR_OMEGA) * oldSolution +
SOR_OMEGA * (VECTOR(target, r) - sum) / MATRIX_SQUARE_N(matrix, r, r, SOR_N)
);
// refresh the solution vector in tuple-space
tuple *solutionBlank = make_tuple("s?", SOLUTION_VECTOR);
get_tuple(solutionBlank, &ctx);
put_tuple(solutionTuple, &ctx);
destroy_tuple(solutionBlank);
// compute difference
real diff = (real) fabs((double)(oldSolution - VECTOR(solution, r)));
if (diff > maxDiff) {
maxDiff = diff;
}
}
}
// flag the output producer.
put_tuple(make_tuple("s", SOR_FLAG), &ctx);
}
void LTRandmat::setup() {
IntVector state = NEW_VECTOR_SZ(INT_TYPE, RANDMAT_NR);
// generate first column values
VECTOR(state, 0) = RAND_SEED % RAND_M;
for (index_t row = 1; row < RANDMAT_NR; ++row) {
VECTOR(state, row) = next(VECTOR(state, row - 1));
}
// generate the A and C values for the next(k) method.
aPrime = RANDMAT_A;
cPrime = 1;
for (index_t i = 1; i < RANDMAT_NR; ++i) {
cPrime = (cPrime + aPrime) % RAND_M;
aPrime = (aPrime * RANDMAT_A) % RAND_M;
}
cPrime = (cPrime * RANDMAT_C) % RAND_M;
// emit the state vector to the tuple space.
tuple *init = make_tuple("ss", "randmat state", "");
init->elements[1].data.s.len = sizeof(INT_TYPE) * RANDMAT_NR;
init->elements[1].data.s.ptr = (char*) state;
put_tuple(init, &ctx);
destroy_tuple(init);
}
void LTMandel::consumeInput() {
// send off a mandelbrot request for each grid row.
for (size_t y = 0; y < MANDEL_NR; ++y) {
tuple *send = make_tuple("si", "mandel request");
send->elements[1].data.i = y;
put_tuple(send, &ctx);
destroy_tuple(send);
}
}
real LTSor::solutionSum(index_t row) {
// tuple template.
tuple *send = make_tuple("sii", "sor request");
// create an "rows reporting" tuple, so that we
// know when the computation should end.
tuple *rowsReporting = make_tuple("si", ROWS_DONE, 0);
put_tuple(rowsReporting, &ctx);
// create a sum tuple
tuple *sumTuple = make_tuple("sd", SOLUTION_SUM, 0.0);
put_tuple(sumTuple, &ctx);
// split points, based on a cluster size of the square-root of the
// number of elements in the solution vector.
index_t skip = (size_t) sqrt((real) SOR_N);
for (index_t pos = 0; pos < SOR_N; pos += skip) {
send->elements[1].data.i = pos;
send->elements[2].data.i = std::min(pos + skip, SOR_N);
put_tuple(send, &ctx);
}
// wait for all the rows to be consumed
rowsReporting->elements[1].data.i = SOR_N;
get_tuple(rowsReporting, &ctx);
// get the sum of the tuple-space op
destroy_tuple(sumTuple);
sumTuple = make_tuple("s?", SOLUTION_SUM);
get_tuple(sumTuple, &ctx);
// return the result
return sumTuple->elements[1].data.d;
}
void LTRandmat::work() {
tuple *recv = make_tuple("s?", REQUEST);
tuple *tmpInit = make_tuple("s?", "randmat state");
tuple *init = NULL;
IntVector initVector = NULL;
// satisfy randmat requests.
while (1) {
// block until we receive a tuple.
tuple* gotten = get_tuple(recv, &ctx);
// grab a copy of the initializer, tuple if we haven't already
if (!init) {
init = read_tuple(tmpInit, &ctx);
initVector = (IntVector) init->elements[1].data.s.ptr;
}
// copy over row co-ordinate of the computation; create
// a buffer for the results of the computation.
size_t row = gotten->elements[1].data.i;
tuple *send = make_tuple("sis", "randmat done", row, "");
IntVector buffer = (IntVector) NEW_VECTOR_SZ(INT_TYPE, RANDMAT_NC);
send->elements[2].data.s.len = sizeof(INT_TYPE) * RANDMAT_NC;
send->elements[2].data.s.ptr = (char*) buffer;
// perform the actual computation for this row.
buffer[0] = VECTOR(initVector, row);
for (int x = 1; x < RANDMAT_NC; ++x) {
buffer[x] = (aPrime * buffer[x-1] + cPrime) % RAND_M;
}
// send off the new tuple and purge local memory of the one we got
put_tuple(send, &ctx);
destroy_tuple(gotten);
destroy_tuple(send);
delete[] buffer;
}
// TODO destroy the template tuples; must send tuples for this
// destroy_tuple(recv);
}
void LTRandmat::consumeInput() {
// create the first, seed column
setup();
// tuple template
tuple *send = make_tuple("si", REQUEST, 0);
// send off a request for each grid row.
for (size_t y = 0; y < RANDMAT_NR; ++y) {
send->elements[1].data.i = y;
put_tuple(send, &ctx);
}
// destroy the template tuple
destroy_tuple(send);
}
void LTMandel::work() {
tuple *recv = make_tuple("s?", "mandel request");
// 2D delta between calculated points
real dX = MANDEL_DX / (MANDEL_NC - 1);
real dY = MANDEL_DY / (MANDEL_NR - 1);
// satisfy mandelbrot requests.
while (1) {
// block until we receive a tuple.
tuple* gotten = get_tuple(recv, &ctx);
// copy over row co-ordinate of the computation; create
// a buffer for the results of the computation.
tuple *send = make_tuple("sis", "mandel done", gotten->elements[1].data.i, "");
int* buffer = (int*) malloc(sizeof(int) * MANDEL_NC);
send->elements[2].data.s.len = sizeof(int) * MANDEL_NC;
send->elements[2].data.s.ptr = (char*) buffer;
// perform the actual computation for this row.
double rY = MANDEL_Y0 + dY * send->elements[1].data.i;
double rX = MANDEL_X0;
for (int x = 0; x < MANDEL_NC; ++x, rX += dX) {
buffer[x] = mandelCalc(rX, rY);
}
// send off the new tuple and purge local memory of the one we gotten
put_tuple(send, &ctx);
destroy_tuple(gotten);
destroy_tuple(send);
}
// TODO destroy the template tuples; must send tuples for this
// destroy_tuple(send);
// destroy_tuple(recv);
}
/**
* Put out requests to have FFT operations done, and measure how
* long it takes to get the results back.
*/
int
main(int argc, char *argv[])
{
struct tuple *s, *t, *u;
int i, j, iters = 0;
int r1[PARALLEL], r2[PARALLEL], r3[PARALLEL];
double x[N], y[N], mult;
struct timeval T0, T1;
int rndsock;
struct context ctx;
if (get_server_portnumber(&ctx)) {
if (argc < 3) {
/* help message */
fprintf(stderr,
"Usage: %s <server> <portnumber>\n", argv[0]);
exit(1);
}
strcpy(ctx.peername, argv[1]);
ctx.portnumber = atoi(argv[2]);
}
rndsock = open("/dev/urandom", O_RDONLY);
mult = 1.0 / pow(2.0, 31);
for (i = 0; i < N; i++) {
x[i] = mult * random_int();
y[i] = mult * random_int();
}
s = make_tuple("siiis#s#", "fft",
0, 0, 0, x, N * sizeof(double), y, N * sizeof(double));
t = make_tuple("siii??", "fft done", 0, 0, 0);
gettimeofday(&T0, NULL);
while (1) {
for (j = 0; j < PARALLEL; j++) {
r1[j] = random_int();
r2[j] = random_int();
r3[j] = random_int();
s->elements[1].data.i = r1[j];
s->elements[2].data.i = r2[j];
s->elements[3].data.i = r3[j];
if (put_tuple(s, &ctx)) {
perror("put_tuple failed");
exit(1);
}
}
for (j = 0; j < PARALLEL; j++) {
t->elements[1].data.i = r1[j];
t->elements[2].data.i = r2[j];
t->elements[3].data.i = r3[j];
u = get_tuple(t, &ctx);
if (u == NULL) {
perror("get_tuple failed");
exit(1);
}
}
gettimeofday(&T1, NULL);
iters += PARALLEL;
printf("%f\n", TIMEVAL_DIFF(T0, T1) / iters);
}
close(rndsock);
destroy_tuple(s);
destroy_tuple(t);
destroy_tuple(u);
return 0;
}
void LTSor::work() {
// tuple templates
tuple *synchLock = make_tuple("s", SYNCH_LOCK);
tuple *recv = make_tuple("s??", "sor request");
// grab pointers locally.
Matrix matrix = (Matrix) inputs[0];
while (1) {
// block until we receieve a tuple.
tuple* gotten = get_tuple(recv, &ctx);
index_t row = gotten->elements[1].data.i;
index_t start = gotten->elements[2].data.i;
index_t stop = gotten->elements[3].data.i;
// grab the solution tuple
tuple *templateSolutionTuple = make_tuple("s?", SOLUTION_VECTOR);
tuple *solutionTuple = read_tuple(templateSolutionTuple, &ctx);
destroy_tuple(templateSolutionTuple);
Vector solution = (Vector) solutionTuple->elements[1].data.s.ptr;
// sum part of the matrix row with the corresponding elements
// in the solution vector.
real sum = 0.0;
for (index_t col = start; col < stop; ++col) {
if (col != row) {
sum += MATRIX_SQUARE_N(matrix, row, col, SOR_N) * VECTOR(solution, col);
}
}
// purge local memory of the tuple we received
destroy_tuple(gotten);
// Now, we combine the results from these elements with the "world".
// enter the critical section
get_tuple(synchLock, &ctx);
// combine results with master copy (sum)
tuple *tmpSum = make_tuple("s?", SOLUTION_SUM);
tuple *tupleSum = get_tuple(tmpSum, &ctx);
tmpSum->elements[1].data.d = sum + tupleSum->elements[1].data.d;
put_tuple(tmpSum, &ctx);
destroy_tuple(tmpSum);
destroy_tuple(tupleSum);
// record the number of rows reporting
tuple *templateRowsReporting = make_tuple("s?", ROWS_DONE);
tuple *rowsReporting = get_tuple(templateRowsReporting, &ctx);
rowsReporting->elements[1].data.i += (stop - start);
put_tuple(rowsReporting, &ctx);
destroy_tuple(templateRowsReporting);
destroy_tuple(rowsReporting);
// leave the critical section
put_tuple(synchLock, &ctx);
}
}
