rtems_task Init(rtems_task_argument ignored)
{
unsigned gdt_limit;
segment_descriptors* gdt_entry_tbl;
uint16_t segment_selector;
uint16_t seg_sel1, seg_sel;
i386_get_info_from_GDTR (&gdt_entry_tbl, &gdt_limit);
segment_descriptors tst1 = {
.type = 0xC, /* bits 4 */
.descriptor_type = 0x1, /* bits 1 */
.privilege = 0x0, /* bits 2 */
.present = 0x1, /* bits 1 */
.available = 0x0, /* bits 1 */
.fixed_value_bits = 0x0, /* bits 1 */
.operation_size = 0x1, /* bits 1 */
};
segment_descriptors test = {
.limit_15_0 = 0x0000, /* bits 16 */
.base_address_15_0 = 0x0000, /* bits 16 */
.base_address_23_16 = 0x00, /* bits 8 */
.type = 0x0, /* bits 4 */
.descriptor_type = 0x1, /* bits 1 */
.privilege = 0x0, /* bits 2 */
.present = 0x1, /* bits 1 */
.limit_19_16 = 0x0, /* bits 4 */
.available = 0x0, /* bits 1 */
.fixed_value_bits = 0x0, /* bits 1 */
.operation_size = 0x0, /* bits 1 */
.granularity = 0x0, /* bits 1 */
.base_address_31_24 = 0x00, /* bits 8 */
};
if(!i386_put_gdt_entry (seg_sel=i386_find_empty_gdt_entry(), 0xff00ff00, 0x0f0f0f, &test)){printk("error inserting descriptor");exit(1);}
if(!i386_put_gdt_entry (seg_sel1=i386_find_empty_gdt_entry(), 0xC0000, 0x0fffff, &tst1)){printk("error inserting descriptor");exit(1);}
if(i386_free_gdt_entry(seg_sel))printk("couldn't free gdt entry\n");
printf("%d", GDT_SIZE);
printf("GDT position: %p\tGDT limit: %d\n", gdt_entry_tbl, gdt_limit);
for(segment_selector=0;segment_selector<(gdt_limit+1)/8;segment_selector++){
printf("(%d)", segment_selector);
printDesc(&gdt_entry_tbl[segment_selector]);
_IBMPC_inch();
}
if(!i386_put_gdt_entry (seg_sel=i386_find_empty_gdt_entry(), 0xccccc0, 0x0111, &test)){printk("error inserting descriptor");exit(1);}
for(segment_selector=0;segment_selector<(gdt_limit+1)/8;segment_selector++){
printf("(%d)", segment_selector);
printDesc(&gdt_entry_tbl[segment_selector]);
_IBMPC_inch();
}
exit(0);
}
void MainWindow::calcDesc()
{
for(int p = 0; p < n; p++) {
//Diag
double temp = A[p][p];
for(int i = 0; i < p; i++) {
temp -= A[p][i] * A[p][i];
}
if(temp >= 0) {
A[p][p] = sqrt(temp);
} else {
ui->LLEdit->append("Eroare la calcul (temp < 0)");
return;
}
//Rest
for(int i = p + 1; i < n; i++) {
temp = A[i][p];
for(int k = 0; k < p; k++) {
temp -= A[i][k] * A[p][k];
}
if(A[p][p] < 0.0000000001 && A[p][p] > -0.0000000001) {
ui->LLEdit->append("Eroare la calcul (A[p][p] == 0)");
return;
} else {
A[i][p] = temp / A[p][p];
}
}
}
printDesc();
}
static void modewhacker( CGDirectDisplayID dspy )
{
int i;
CFDictionaryRef mode;
CFDictionaryRef originalMode;
boolean_t exactMatch;
CGDisplayErr err;
originalMode = CGDisplayCurrentMode( dspy );
if ( originalMode == NULL )
{
printf( "Display is invalid\n" );
return;
}
for ( i = 0; i < (sizeof myModeList / sizeof myModeList[0]); ++i )
{
printf( "Display 0x%x: Looking for %ld x %ld, %ld Bits Per Pixel\n",
(unsigned int)dspy,
myModeList[i].width,
myModeList[i].height,
myModeList[i].bitsPerPixel );
mode = CGDisplayBestModeForParameters(dspy,
myModeList[i].bitsPerPixel,
myModeList[i].width,
myModeList[i].height,
&exactMatch);
if ( exactMatch )
printf( "Found an exact match, switching modes:\n" );
else
printf( "Found a mode, switching modes:\n" );
printDesc( mode );
sleep( 1 ); // Short pause, then switch
err = CGDisplaySwitchToMode(dspy, mode);
if ( err != CGDisplayNoErr )
{
printf( "Oops! Mode switch failed?!?? (%d)\n", err );
break;
}
printf( "Pausing for 5 seconds...\n" );
sleep( 5 );
}
err = CGDisplaySwitchToMode(dspy, originalMode);
if ( err != CGDisplayNoErr )
printf( "Oops! Mode restore failed?!?? (%d)\n", err );
}
void TransRecord::printRecord()
{
My570ListElem *item = NULL;
TransNode *node = NULL;
int balance = 0;
for (item = _list.First(); item != NULL; item = _list.Next(item))
{
node = (TransNode *)item->Obj();
if (node->_type == '+') {
balance += node->_amount;
} else if (node->_type == '-'){
balance -= node->_amount;
} else {
cerr << "error transaction type\n";
exit(-1);
}
printDate(node);
printDesc(node);
printAmount(node);
printBalance(balance);
}
}
int main(int argc, char *argv[]){
Params params;
std::map<std::string, std::string> args;
readArgs(argc, argv, args);
if(args.find("algo")!=args.end()){
params.algo = args["algo"];
}else{
params.algo = "qdMCNat";
}
if(args.find("inst_file")!=args.end())
setParamsFromFile(args["inst_file"], args, params);
else
setParams(params.algo, args, params);
createLogDir(params.dir_path);
gen.seed(params.seed);
// Load the dataset
MyMatrix X_train, X_valid;
VectorXd Y_train, Y_valid;
loadMnist(params.ratio_train, X_train, X_valid, Y_train, Y_valid);
//loadCIFAR10(params.ratio_train, X_train, X_valid, Y_train, Y_valid);
//loadLightCIFAR10(params.ratio_train, X_train, X_valid, Y_train, Y_valid);
// ConvNet parameters
std::vector<ConvLayerParams> conv_params;
ConvLayerParams conv_params1;
conv_params1.Hf = 5;
conv_params1.stride = 1;
conv_params1.n_filter = 20;
conv_params1.padding = 0;
conv_params.push_back(conv_params1);
ConvLayerParams conv_params2;
conv_params2.Hf = 5;
conv_params2.stride = 1;
conv_params2.n_filter = 50;
conv_params2.padding = 0;
conv_params.push_back(conv_params2);
std::vector<PoolLayerParams> pool_params;
PoolLayerParams pool_params1;
pool_params1.Hf = 2;
pool_params1.stride = 2;
pool_params.push_back(pool_params1);
PoolLayerParams pool_params2;
pool_params2.Hf = 2;
pool_params2.stride = 2;
pool_params.push_back(pool_params2);
const unsigned n_conv_layer = conv_params.size();
for(unsigned l = 0; l < conv_params.size(); l++){
if(l==0){
conv_params[l].filter_size = conv_params[l].Hf * conv_params[l].Hf * params.img_depth;
conv_params[l].N = (params.img_width - conv_params[l].Hf + 2*conv_params[l].padding)/conv_params[l].stride + 1;
}
else{
conv_params[l].filter_size = conv_params[l].Hf * conv_params[l].Hf * conv_params[l-1].n_filter;
conv_params[l].N = (pool_params[l-1].N - conv_params[l].Hf + 2*conv_params[l].padding)/conv_params[l].stride + 1;
}
pool_params[l].N = (conv_params[l].N - pool_params[l].Hf)/pool_params[l].stride + 1;
}
// Neural Network parameters
const unsigned n_training = X_train.rows();
const unsigned n_valid = X_valid.rows();
const unsigned n_feature = X_train.cols();
const unsigned n_label = Y_train.maxCoeff() + 1;
params.nn_arch.insert(params.nn_arch.begin(),conv_params[n_conv_layer-1].n_filter * pool_params[n_conv_layer-1].N * pool_params[n_conv_layer-1].N);
params.nn_arch.push_back(n_label);
const unsigned n_layers = params.nn_arch.size();
// Optimization parameter
const int n_train_batch = ceil(n_training/(float)params.train_minibatch_size);
const int n_valid_batch = ceil(n_valid/(float)params.valid_minibatch_size);
double prev_loss = std::numeric_limits<double>::max();
double eta = params.eta;
// Create the convolutional layer
std::vector<MyMatrix> conv_W(n_conv_layer);
std::vector<MyMatrix> conv_W_T(n_conv_layer);
std::vector<MyVector> conv_B(n_conv_layer);
// Create the neural network
MyMatrix W_out(params.nn_arch[n_layers-2],n_label);
std::vector<MySpMatrix> W(n_layers-2);
std::vector<MySpMatrix> Wt(n_layers-2);
std::vector<MyVector> B(n_layers-1);
double init_sigma = 0.;
ActivationFunction act_func;
ActivationFunction eval_act_func;
if(params.act_func_name=="sigmoid"){
init_sigma = 4.0;
act_func = std::bind(logistic,true,_1,_2,_3);
eval_act_func = std::bind(logistic,false,_1,_2,_3);
}else if(params.act_func_name=="tanh"){
init_sigma = 1.0;
act_func = std::bind(my_tanh,true,_1,_2,_3);
eval_act_func = std::bind(my_tanh,false,_1,_2,_3);
}else if(params.act_func_name=="relu"){
init_sigma = 1.0; // TODO: Find the good value
act_func = std::bind(relu,true,_1,_2,_3);
eval_act_func = std::bind(relu,false,_1,_2,_3);
}else{
std::cout << "Not implemented yet!" << std::endl;
assert(false);
}
std::cout << "Initializing the network... ";
params.n_params = initNetwork(params.nn_arch, params.act_func_name, params.sparsity, conv_params, pool_params, W_out, W, Wt, B, conv_W, conv_W_T, conv_B); // TODO: Init the conv bias
// Deep copy of parameters for the adaptive rule
std::vector<MyMatrix> mu_dW(n_layers-1);
std::vector<MyVector> mu_dB(n_layers-1);
MyMatrix pW_out = W_out;
std::vector<MySpMatrix> pW = W;
std::vector<MySpMatrix> pWt = Wt;
std::vector<MyVector> pB = B;
MyMatrix ppMii_out, ppM0i_out;
MyVector ppM00_out;
std::vector<MySpMatrix> ppMii,ppM0i;
std::vector<MyVector> ppM00;
MyMatrix pMii_out,pM0i_out;
MyVector pM00_out;
std::vector<MySpMatrix> pMii,pM0i;
std::vector<MyVector> pM00;
std::vector<MyMatrix> conv_ppMii, conv_ppM0i;
std::vector<MyVector> conv_ppM00;
std::vector<MyMatrix> conv_pMii, conv_pM0i;
std::vector<MyVector> conv_pM00;
// Convert the labels to one-hot vector
MyMatrix one_hot = MyMatrix::Zero(n_training, n_label);
labels2oneHot(Y_train,one_hot);
// Configure the logger
std::ostream* logger;
if(args.find("verbose")!=args.end()){
getOutput("",logger);
}else{
getOutput(params.file_path,logger);
}
double cumul_time = 0.;
printDesc(params, logger);
printConvDesc(params, conv_params, pool_params, logger);
std::cout << "Starting the learning phase... " << std::endl;
*logger << "Epoch Time(s) train_loss train_accuracy valid_loss valid_accuracy eta" << std::endl;
for(unsigned i = 0; i < params.n_epoch; i++){
for(unsigned j = 0; j < n_train_batch; j++){
// Mini-batch creation
unsigned curr_batch_size = 0;
MyMatrix X_batch, one_hot_batch;
getMiniBatch(j, params.train_minibatch_size, X_train, one_hot, params, conv_params[0], curr_batch_size, X_batch, one_hot_batch);
double prev_time = gettime();
// Forward propagation for conv layer
std::vector<std::vector<unsigned>> poolIdxX1(n_conv_layer);
std::vector<std::vector<unsigned>> poolIdxY1(n_conv_layer);
MyMatrix z0;
std::vector<MyMatrix> conv_A(conv_W.size());
std::vector<MyMatrix> conv_Ap(conv_W.size());
convFprop(curr_batch_size, conv_params, pool_params, act_func, conv_W, conv_B, X_batch, conv_A, conv_Ap, z0, poolIdxX1, poolIdxY1);
// Forward propagation
std::vector<MyMatrix> Z(n_layers-1);
std::vector<MyMatrix> A(n_layers-2);
std::vector<MyMatrix> Ap(n_layers-2);
fprop(params.dropout_flag, act_func, W, W_out, B, z0, Z, A, Ap);
// Compute the output and the error
MyMatrix out;
softmax(Z[n_layers-2], out);
std::vector<MyMatrix> gradB(n_layers-1);
gradB[n_layers-2] = out - one_hot_batch;
// Backpropagation
bprop(Wt, W_out, Ap, gradB);
// Backpropagation for conv layer
std::vector<MyMatrix> conv_gradB(conv_W.size());
MyMatrix layer_gradB = (gradB[0] * W[0].transpose());
MyMatrix pool_gradB;
layer2pool(curr_batch_size, pool_params[conv_W.size()-1].N, conv_params[conv_W.size()-1].n_filter, layer_gradB, pool_gradB);
convBprop(curr_batch_size, conv_params, pool_params, conv_W_T, conv_Ap, pool_gradB, conv_gradB, poolIdxX1, poolIdxY1);
if(params.algo == "bprop"){
update(eta, gradB, A, z0, params.regularizer, params.lambda, W_out, W, Wt, B);
convUpdate(curr_batch_size, eta, conv_params, conv_gradB, conv_A, X_batch, "", 0., conv_W, conv_W_T, conv_B);
}else{
// Compute the metric
std::vector<MyMatrix> metric_gradB(n_layers-1);
std::vector<MyMatrix> metric_conv_gradB(conv_params.size());
if(params.algo=="qdMCNat"){
// Monte-Carlo Approximation of the metric
std::vector<MyMatrix> mc_gradB(n_layers-1);
computeMcError(out, mc_gradB[n_layers-2]);
// Backpropagation
bprop(Wt, W_out, Ap, mc_gradB);
for(unsigned k = 0; k < gradB.size(); k++){
metric_gradB[k] = mc_gradB[k].array().square();
}
// Backpropagation for conv layer
std::vector<MyMatrix> mc_conv_gradB(conv_W.size());
MyMatrix mc_layer_gradB = (mc_gradB[0] * W[0].transpose());
MyMatrix mc_pool_gradB;
layer2pool(curr_batch_size, pool_params[conv_W.size()-1].N, conv_params[conv_W.size()-1].n_filter, mc_layer_gradB, mc_pool_gradB);
convBprop(curr_batch_size, conv_params, pool_params, conv_W_T, conv_Ap, mc_pool_gradB, mc_conv_gradB, poolIdxX1, poolIdxY1);
for(unsigned k = 0; k < conv_params.size(); k++){
metric_conv_gradB[k] = mc_conv_gradB[k].array().square();
}
}
else if(params.algo=="qdop"){
for(unsigned k = 0; k < conv_params.size(); k++){
metric_conv_gradB[k] = conv_gradB[k].array().square();
}
for(unsigned k = 0; k < gradB.size(); k++){
metric_gradB[k] = gradB[k].array().square();
}
}
else if(params.algo=="qdNat"){
for(unsigned k = 0; k < conv_params.size(); k++){
metric_conv_gradB[k] = conv_gradB[k].array().square();
}
for(unsigned k = 0; k < metric_gradB.size(); k++){
metric_gradB[k] = MyMatrix::Zero(gradB[k].rows(),gradB[k].cols());
}
for(unsigned l = 0; l < n_label; l++){
MyMatrix fisher_ohbatch = MyMatrix::Zero(curr_batch_size, n_label);
fisher_ohbatch.col(l).setOnes();
std::vector<MyMatrix> fgradB(n_layers-1);
fgradB[n_layers-2] = out - fisher_ohbatch;
bprop(Wt, W_out, Ap, fgradB);
// Backpropagation for conv layer
std::vector<MyMatrix> fisher_conv_gradB(conv_W.size());
MyMatrix fisher_layer_gradB = (fgradB[0] * W[0].transpose());
MyMatrix fisher_pool_gradB;
layer2pool(curr_batch_size, pool_params[conv_W.size()-1].N, conv_params[conv_W.size()-1].n_filter, fisher_layer_gradB, fisher_pool_gradB);
convBprop(curr_batch_size, conv_params, pool_params, conv_W_T, conv_Ap, fisher_pool_gradB, fisher_conv_gradB, poolIdxX1, poolIdxY1);
for(unsigned k = 0; k < conv_params.size(); k++){
MyMatrix fisher_conv_gradB_sq = fisher_conv_gradB[k].array().square();
for(unsigned m = 0; m < out.rows(); m++){
for(unsigned f = 0; f < conv_params[k].n_filter; f++){
for(unsigned n = 0; n < conv_params[k].N * conv_params[k].N; n++){
fisher_conv_gradB_sq(f,m*conv_params[k].N*conv_params[k].N+n) *= out(m,l);
}
}
}
metric_conv_gradB[k] += fisher_conv_gradB_sq;
}
for(unsigned k = 0; k < W.size(); k++){
const unsigned rev_k = n_layers - k - 2;
metric_gradB[rev_k] += (fgradB[rev_k].array().square().array().colwise() * out.array().col(l)).matrix();
}
}
}
bool init_flag = false;
if(i == 0 && j == 0 && !params.init_metric_id){
init_flag = true;
}
std::vector<MyMatrix> conv_Mii(conv_params.size());
std::vector<MyMatrix> conv_M0i(conv_params.size());
std::vector<MyVector> conv_M00(conv_params.size());
buildConvQDMetric(curr_batch_size, metric_conv_gradB, conv_A, X_batch, conv_W, params.matrix_reg, conv_Mii, conv_M0i, conv_M00);
updateConvMetric(init_flag, params.metric_gamma, conv_pMii, conv_pM0i, conv_pM00, conv_Mii, conv_M0i, conv_M00);
MyMatrix Mii_out, M0i_out;
MyVector M00_out;
std::vector<MySpMatrix> Mii(W.size());
std::vector<MySpMatrix> M0i(W.size());
std::vector<MyVector> M00(W.size());
buildQDMetric(metric_gradB, A, z0, W_out, W, params.matrix_reg, Mii_out, M0i_out, M00_out, Mii, M0i, M00);
updateMetric(init_flag, params.metric_gamma, Mii_out, M0i_out, M00_out, Mii, M0i, M00, pMii_out, pM0i_out, pM00_out, pMii, pM0i, pM00);
update(eta, gradB, A, z0, params.regularizer, params.lambda, W_out, W, Wt, B, Mii_out, M0i_out, M00_out, Mii, M0i, M00);
}
double curr_time = gettime();
cumul_time += curr_time - prev_time;
if(params.minilog_flag){
double train_loss = 0.;
double train_accuracy = 0.;
double valid_loss = 0.;
double valid_accuracy = 0.;
evalModel(eval_act_func, params, n_train_batch, n_training, X_train, Y_train, conv_params, pool_params, conv_W, conv_B, W_out, W, B, train_loss, train_accuracy);
evalModel(eval_act_func, params, n_valid_batch, n_valid, X_valid, Y_valid, conv_params, pool_params, conv_W, conv_B, W_out, W, B, valid_loss, valid_accuracy);
// Logging
*logger << i + float(j)/n_train_batch << " " << cumul_time << " " << train_loss << " " << train_accuracy << " " << valid_loss << " " << valid_accuracy << " " << eta << std::endl;
}
}
if(!params.minilog_flag || params.adaptive_flag){
double train_loss = 0.;
double train_accuracy = 0.;
double valid_loss = 0.;
double valid_accuracy = 0.;
evalModel(eval_act_func, params, n_train_batch, n_training, X_train, Y_train, conv_params, pool_params, conv_W, conv_B, W_out, W, B, train_loss, train_accuracy);
evalModel(eval_act_func, params, n_valid_batch, n_valid, X_valid, Y_valid, conv_params, pool_params, conv_W, conv_B, W_out, W, B, valid_loss, valid_accuracy);
// if(params.adaptive_flag)
// adaptiveRule(train_loss, prev_loss, eta, W, B, pMii, pM0i, pM00, pW, pB, ppMii, ppM0i, ppM00);
// Logging
if(!params.minilog_flag){
*logger << i << " " << cumul_time << " " << train_loss << " " << train_accuracy << " " << valid_loss << " " << valid_accuracy << " " << eta << std::endl;
}
}
}
}
int main(int argc, char* argv[]) {
const int SIZE = 1 << 8;
const int NPOT = SIZE - 3;
int a[SIZE], b[SIZE], c[SIZE];
float ms_time = 0.0f;
// Scan tests
printf("\n");
printf("****************\n");
printf("** SCAN TESTS **\n");
printf("****************\n");
genArray(SIZE - 1, a, 50); // Leave a 0 at the end to test that edge case
printArray(SIZE, a, true);
zeroArray(SIZE, b);
printDesc("cpu scan, power-of-two");
ms_time = StreamCompaction::CPU::scan(SIZE, b, a);
printf("CPU execution time for scan: %.5fms\n", ms_time);
printArray(SIZE, b, true);
zeroArray(SIZE, c);
printDesc("cpu scan, non-power-of-two");
ms_time = StreamCompaction::CPU::scan(NPOT, c, a);
printf("CPU execution time for scan: %.5fms\n", ms_time);
printArray(NPOT, b, true);
printCmpResult(NPOT, b, c);
zeroArray(SIZE, c);
printDesc("naive scan, power-of-two");
StreamCompaction::Naive::scan(SIZE, c, a);
//printArray(SIZE, c, true);
printCmpResult(SIZE, b, c);
zeroArray(SIZE, c);
printDesc("naive scan, non-power-of-two");
StreamCompaction::Naive::scan(NPOT, c, a);
//printArray(SIZE, c, true);
printCmpResult(NPOT, b, c);
zeroArray(SIZE, c);
printDesc("work-efficient scan, power-of-two");
ms_time = StreamCompaction::Efficient::scan(SIZE, c, a);
printf("CUDA execution time for work efficient scan: %.5fms\n", ms_time);
//printArray(SIZE, c, true);
printCmpResult(SIZE, b, c);
zeroArray(SIZE, c);
printDesc("work-efficient scan, non-power-of-two");
ms_time = StreamCompaction::Efficient::scan(NPOT, c, a);
printf("CUDA execution time for work efficient scan: %.5fms\n", ms_time);
//printArray(NPOT, c, true);
printCmpResult(NPOT, b, c);
zeroArray(SIZE, c);
printDesc("thrust scan, power-of-two");
StreamCompaction::Thrust::scan(SIZE, c, a);
//printArray(SIZE, c, true);
printCmpResult(SIZE, b, c);
zeroArray(SIZE, c);
printDesc("thrust scan, non-power-of-two");
StreamCompaction::Thrust::scan(NPOT, c, a);
//printArray(NPOT, c, true);
printCmpResult(NPOT, b, c);
printf("\n");
printf("*****************************\n");
printf("** STREAM COMPACTION TESTS **\n");
printf("*****************************\n");
// Compaction tests
genArray(SIZE - 1, a, 4); // Leave a 0 at the end to test that edge case
printArray(SIZE, a, true);
int count, expectedCount, expectedNPOT;
zeroArray(SIZE, b);
printDesc("cpu compact without scan, power-of-two");
count = StreamCompaction::CPU::compactWithoutScan(SIZE, b, a);
expectedCount = count;
printArray(count, b, true);
printCmpLenResult(count, expectedCount, b, b);
zeroArray(SIZE, c);
printDesc("cpu compact without scan, non-power-of-two");
count = StreamCompaction::CPU::compactWithoutScan(NPOT, c, a);
expectedNPOT = count;
printArray(count, c, true);
printCmpLenResult(count, expectedNPOT, b, c);
zeroArray(SIZE, c);
printDesc("cpu compact with scan");
count = StreamCompaction::CPU::compactWithScan(SIZE, c, a);
printArray(count, c, true);
printCmpLenResult(count, expectedCount, b, c);
zeroArray(SIZE, c);
printDesc("work-efficient compact, power-of-two");
count = StreamCompaction::Efficient::compact(SIZE, c, a);
//printArray(count, c, true);
printCmpLenResult(count, expectedCount, b, c);
zeroArray(SIZE, c);
printDesc("work-efficient compact, non-power-of-two");
count = StreamCompaction::Efficient::compact(NPOT, c, a);
//printArray(count, c, true);
printCmpLenResult(count, expectedNPOT, b, c);
}
int main(int argc, char* argv[]) {
const int SIZE = 1 << 10;
const int NPOT = SIZE - 3;
int a[SIZE], b[SIZE], c[SIZE];
// Scan tests
printf("\n");
printf("****************\n");
printf("** SCAN TESTS **\n");
printf("****************\n");
genArray(SIZE - 1, a, 50); // Leave a 0 at the end to test that edge case
a[SIZE - 1] = 0;
printArray(SIZE, a, true);
zeroArray(SIZE, b);
printDesc("cpu scan, power-of-two");
StreamCompaction::CPU::scan(SIZE, b, a);
//printArray(SIZE, b, true);
zeroArray(SIZE, c);
printDesc("cpu scan, non-power-of-two");
StreamCompaction::CPU::scan(NPOT, c, a);
//printArray(NPOT, b, true);
printCmpResult(NPOT, b, c);
zeroArray(SIZE, c);
printDesc("naive scan, power-of-two");
StreamCompaction::Naive::scan(SIZE, c, a);
//printArray(SIZE, c, true);
printCmpResult(SIZE, b, c);
zeroArray(SIZE, c);
printDesc("naive scan, non-power-of-two");
StreamCompaction::Naive::scan(NPOT, c, a);
//printArray(NPOT, c, true);
printCmpResult(NPOT, b, c);
zeroArray(SIZE, c);
printDesc("work-efficient scan, power-of-two");
StreamCompaction::Efficient::scan(SIZE, c, a);
//printArray(SIZE, c, true);
printCmpResult(SIZE, b, c);
zeroArray(SIZE, c);
printDesc("work-efficient scan, non-power-of-two");
StreamCompaction::Efficient::scan(NPOT, c, a);
//printArray(NPOT, c, true);
printCmpResult(NPOT, b, c);
zeroArray(SIZE, c);
printDesc("thrust scan, power-of-two");
StreamCompaction::Thrust::scan(SIZE, c, a);
//printArray(SIZE, c, true);
printCmpResult(SIZE, b, c);
zeroArray(SIZE, c);
printDesc("thrust scan, non-power-of-two");
StreamCompaction::Thrust::scan(NPOT, c, a);
//printArray(NPOT, c, true);
printCmpResult(NPOT, b, c);
//*
printf("\n");
printf("*****************************\n");
printf("** STREAM COMPACTION TESTS **\n");
printf("*****************************\n");
// Compaction tests
genArray(SIZE - 1, a, 4); // Leave a 0 at the end to test that edge case
printArray(SIZE, a, true);
a[SIZE - 1] = 0;
int count, expectedCount, expectedNPOT;
zeroArray(SIZE, b);
printDesc("cpu compact without scan, power-of-two");
count = StreamCompaction::CPU::compactWithoutScan(SIZE, b, a);
expectedCount = count;
printArray(count, b, true);
printCmpLenResult(count, expectedCount, b, b);
zeroArray(SIZE, c);
printDesc("cpu compact without scan, non-power-of-two");
count = StreamCompaction::CPU::compactWithoutScan(NPOT, c, a);
expectedNPOT = count;
printArray(count, c, true);
printCmpLenResult(count, expectedNPOT, b, c);
zeroArray(SIZE, c);
printDesc("cpu compact with scan");
count = StreamCompaction::CPU::compactWithScan(SIZE, c, a);
printArray(count, c, true);
printCmpLenResult(count, expectedCount, b, c);
zeroArray(SIZE, c);
printDesc("work-efficient compact, power-of-two");
printArray(SIZE, a, true);//delete
count = StreamCompaction::Efficient::compact(SIZE, c, a);
printArray(count, c, true);
printCmpLenResult(count, expectedCount, b, c);
zeroArray(SIZE, c);
printDesc("work-efficient compact, non-power-of-two");
printArray(SIZE, a, true);//delete
count = StreamCompaction::Efficient::compact(NPOT, c, a);
printArray(count, c, true);
printCmpLenResult(count, expectedNPOT, b, c);
printf("\n");
printf("*****************************\n");
printf("** Radix Sort **\n");
printf("*****************************\n");
a[0] = 4;
a[1] = 7;
a[2] = 2;
a[3] = 6;
a[4] = 3;
a[5] = 5;
a[6] = 1;
a[7] = 0;
zeroArray(8, c);
printDesc("Radix Sort, power-of-two");
printArray(8, a, true);//delete
StreamCompaction::RadixSort::sort(8, c, a);
printArray(8, c, true);
//printCmpResult(SIZE, b, c);*/
std::cin.get();
}
