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update optimize code
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@minmingzhu
minmingzhu committed Sep 10, 2024
1 parent 8b5d91d commit 6e1eb5b
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Showing 23 changed files with 82 additions and 117 deletions.
22 changes: 8 additions & 14 deletions mllib-dal/src/main/native/CorrelationImpl.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -197,9 +197,9 @@ static void doCorrelationOneAPICompute(

JNIEXPORT jlong JNICALL
Java_com_intel_oap_mllib_stat_CorrelationDALImpl_cCorrelationTrainDAL(
JNIEnv *env, jobject obj, jint rank, jlong pNumTabData, jlong numRows,
jlong numCols, jint executorNum, jint executorCores,
jint computeDeviceOrdinal, jintArray gpuIdxArray, jobject resultObj) {
JNIEnv *env, jobject obj, jint rank, jlong pNumTabData, jlong numRows, jlong numCols,
jint executorNum, jint executorCores, jint computeDeviceOrdinal,
jintArray gpuIdxArray, jstring ip_port, jobject resultObj) {
logger::println(logger::INFO,
"oneDAL (native): use DPC++ kernels; device %s",
ComputeDeviceString[computeDeviceOrdinal].c_str());
Expand All @@ -225,23 +225,17 @@ Java_com_intel_oap_mllib_stat_CorrelationDALImpl_cCorrelationTrainDAL(
}
#ifdef CPU_GPU_PROFILE
case ComputeDevice::gpu: {
int nGpu = env->GetArrayLength(gpuIdxArray);
logger::println(
logger::INFO,
"oneDAL (native): use GPU kernels with %d GPU(s) rankid %d", nGpu,
rank);
"oneDAL (native): use GPU kernels with rankid %d", rank);

jint *gpuIndices = env->GetIntArrayElements(gpuIdxArray, 0);
const char *str = env->GetStringUTFChars(ip_port, nullptr);
ccl::string ccl_ip_port(str);
auto comm = createDalCommunicator(executorNum, rank, ccl_ip_port);

auto queue = getAssignedGPU(device, gpuIndices);

ccl::shared_ptr_class<ccl::kvs> &kvs = getKvs();
auto comm =
preview::spmd::make_communicator<preview::spmd::backend::ccl>(
queue, executorNum, rank, kvs);
doCorrelationOneAPICompute(env, pNumTabData, numRows, numCols, comm,
resultObj);
env->ReleaseIntArrayElements(gpuIdxArray, gpuIndices, 0);
env->ReleaseStringUTFChars(ip_port, str);
break;
}
#endif
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19 changes: 6 additions & 13 deletions mllib-dal/src/main/native/DecisionForestClassifierImpl.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -307,35 +307,28 @@ Java_com_intel_oap_mllib_classification_RandomForestClassifierDALImpl_cRFClassif
jint minObservationsSplitNode, jdouble minWeightFractionLeafNode,
jdouble minImpurityDecreaseSplitNode, jint maxTreeDepth, jlong seed,
jint maxBins, jboolean bootstrap, jintArray gpuIdxArray,
jobject resultObj) {
jstring ip_port, jobject resultObj) {
logger::println(logger::INFO, "oneDAL (native): use DPC++ kernels");

ComputeDevice device = getComputeDeviceByOrdinal(computeDeviceOrdinal);
switch (device) {
case ComputeDevice::gpu: {
int nGpu = env->GetArrayLength(gpuIdxArray);
logger::println(
logger::INFO,
"oneDAL (native): use GPU kernels with %d GPU(s) rankid %d", nGpu,
rank);

jint *gpuIndices = env->GetIntArrayElements(gpuIdxArray, 0);

ComputeDevice device = getComputeDeviceByOrdinal(computeDeviceOrdinal);
"oneDAL (native): use GPU kernels with rankid %d", rank);

auto queue = getAssignedGPU(device, gpuIndices);
const char *str = env->GetStringUTFChars(ip_port, nullptr);
ccl::string ccl_ip_port(str);
auto comm = createDalCommunicator(executorNum, rank, ccl_ip_port);

ccl::shared_ptr_class<ccl::kvs> &kvs = getKvs();
auto comm =
preview::spmd::make_communicator<preview::spmd::backend::ccl>(
queue, executorNum, rank, kvs);
jobject hashmapObj = doRFClassifierOneAPICompute(
env, pNumTabFeature, featureRows, featureCols, pNumTabLabel,
labelCols, executorNum, computeDeviceOrdinal, classCount, treeCount,
numFeaturesPerNode, minObservationsLeafNode,
minObservationsSplitNode, minWeightFractionLeafNode,
minImpurityDecreaseSplitNode, maxTreeDepth, seed, maxBins,
bootstrap, comm, resultObj);
env->ReleaseStringUTFChars(ip_port, str);
return hashmapObj;
}
default: {
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26 changes: 10 additions & 16 deletions mllib-dal/src/main/native/DecisionForestRegressorImpl.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -292,38 +292,32 @@ static jobject doRFRegressorOneAPICompute(

JNIEXPORT jobject JNICALL
Java_com_intel_oap_mllib_regression_RandomForestRegressorDALImpl_cRFRegressorTrainDAL(
JNIEnv *env, jobject obj, jint rank, jlong pNumTabFeature,
jlong featureRows, jlong featureCols, jlong pNumTabLabel, jlong labelCols,
jint executorNum, jint computeDeviceOrdinal, jint treeCount,
jint numFeaturesPerNode, jint minObservationsLeafNode, jint maxTreeDepth,
jlong seed, jint maxbins, jboolean bootstrap, jintArray gpuIdxArray,
jobject resultObj) {
JNIEnv *env, jobject obj, jint rank, jlong pNumTabFeature, jlong featureRows,
jlong featureCols, jlong pNumTabLabel, jlong labelCols, jint executorNum,
jint computeDeviceOrdinal, jint treeCount, jint numFeaturesPerNode,
jint minObservationsLeafNode, jint maxTreeDepth, jlong seed, jint maxbins,
jboolean bootstrap, jintArray gpuIdxArray, jstring ip_port, jobject resultObj) {
logger::println(logger::INFO,
"OneDAL (native): use DPC++ kernels; device %s",
ComputeDeviceString[computeDeviceOrdinal].c_str());

ComputeDevice device = getComputeDeviceByOrdinal(computeDeviceOrdinal);
switch (device) {
case ComputeDevice::gpu: {
int nGpu = env->GetArrayLength(gpuIdxArray);
logger::println(
logger::INFO,
"OneDAL (native): use GPU kernels with %d GPU(s) rankid %d", nGpu,
rank);

jint *gpuIndices = env->GetIntArrayElements(gpuIdxArray, 0);
"OneDAL (native): use GPU kernels with rankid %d", rank);

auto queue = getAssignedGPU(device, gpuIndices);
const char *str = env->GetStringUTFChars(ip_port, nullptr);
ccl::string ccl_ip_port(str);
auto comm = createDalCommunicator(executorNum, rank, ccl_ip_port);

ccl::shared_ptr_class<ccl::kvs> &kvs = getKvs();
auto comm =
preview::spmd::make_communicator<preview::spmd::backend::ccl>(
queue, executorNum, rank, kvs);
jobject hashmapObj = doRFRegressorOneAPICompute(
env, pNumTabFeature, featureRows, featureCols, pNumTabLabel,
labelCols, executorNum, computeDeviceOrdinal, treeCount,
numFeaturesPerNode, minObservationsLeafNode, maxTreeDepth, seed,
maxbins, bootstrap, comm, resultObj);
env->ReleaseStringUTFChars(ip_port, str);
return hashmapObj;
}
default: {
Expand Down
6 changes: 0 additions & 6 deletions mllib-dal/src/main/native/GPU.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -25,7 +25,6 @@ static std::vector<sycl::device> get_gpus() {
}

static int getLocalRank(ccl::communicator &comm, int size, int rank) {
const int MPI_MAX_PROCESSOR_NAME = 128;
/* Obtain local rank among nodes sharing the same host name */
char zero = static_cast<char>(0);
std::vector<char> name(MPI_MAX_PROCESSOR_NAME + 1, zero);
Expand Down Expand Up @@ -128,8 +127,6 @@ preview::spmd::communicator<preview::spmd::device_memory_access::usm> createDalC

logger::println(logger::INFO, "OneCCL singleton init took %f secs",
duration / 1000);
logger::Logger::getInstance(c_breakdown_name).printLogToFile("rankID was %d, OneCCL singleton init took %f secs.", rank, duration / 1000 );


t1 = std::chrono::high_resolution_clock::now();

Expand All @@ -145,7 +142,6 @@ preview::spmd::communicator<preview::spmd::device_memory_access::usm> createDalC
.count();
logger::println(logger::INFO, "OneCCL (native): create kvs took %f secs",
duration / 1000);
logger::Logger::getInstance(c_breakdown_name).printLogToFile("rankID was %d, OneCCL create communicator took %f secs.", rank, duration / 1000 );
sycl::queue queue{gpus[0]};
t1 = std::chrono::high_resolution_clock::now();
auto comm =
Expand All @@ -155,7 +151,5 @@ preview::spmd::communicator<preview::spmd::device_memory_access::usm> createDalC
duration =
(float)std::chrono::duration_cast<std::chrono::milliseconds>(t2 - t1)
.count();
logger::Logger::getInstance(c_breakdown_name).printLogToFile("rankID was %d, create communicator took %f secs.", rank, duration / 1000 );
return comm;
}

8 changes: 4 additions & 4 deletions mllib-dal/src/main/native/KMeansImpl.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -305,10 +305,10 @@ static jlong doKMeansOneAPICompute(
*/
JNIEXPORT jlong JNICALL
Java_com_intel_oap_mllib_clustering_KMeansDALImpl_cKMeansOneapiComputeWithInitCenters(
JNIEnv *env, jobject obj, jint rank, jlong pNumTabData, jlong numRows,
jlong numCols, jlong pNumTabCenters, jint clusterNum, jdouble tolerance,
jint iterationNum, jint executorNum, jint executorCores,
jint computeDeviceOrdinal, jintArray gpuIdxArray, jobject resultObj) {
JNIEnv *env, jobject obj, jint rank, jlong pNumTabData, jlong numRows, jlong numCols,
jlong pNumTabCenters, jint clusterNum, jdouble tolerance, jint iterationNum,
jint executorNum, jint executorCores, jint computeDeviceOrdinal,
jintArray gpuIdxArray, jstring ip_port, jobject resultObj) {
logger::println(logger::INFO,
"OneDAL (native): use DPC++ kernels; device %s",
ComputeDeviceString[computeDeviceOrdinal].c_str());
Expand Down
25 changes: 11 additions & 14 deletions mllib-dal/src/main/native/LinearRegressionImpl.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -214,7 +214,8 @@ ridge_regression_compute(size_t rankId, ccl::communicator &comm,
}

#ifdef CPU_GPU_PROFILE
static jlong doLROneAPICompute(JNIEnv *env, size_t rankId, sycl::queue &queue,
static jlong doLROneAPICompute(JNIEnv *env, size_t rankId,
preview::spmd::communicator<preview::spmd::device_memory_access::usm> comm,
jlong pNumTabFeature, jlong featureRows,
jlong featureCols, jlong pNumTabLabel,
jlong labelCols, jboolean jfitIntercept,
Expand All @@ -224,9 +225,6 @@ static jlong doLROneAPICompute(JNIEnv *env, size_t rankId, sycl::queue &queue,
const bool isRoot = (rankId == ccl_root);
bool fitIntercept = bool(jfitIntercept);

ccl::shared_ptr_class<ccl::kvs> &kvs = getKvs();
auto comm = preview::spmd::make_communicator<preview::spmd::backend::ccl>(
queue, executorNum, rankId, kvs);
homogen_table xtrain = *reinterpret_cast<homogen_table *>(
createHomogenTableWithArrayPtr(pNumTabFeature, featureRows, featureCols,
comm.get_queue())
Expand Down Expand Up @@ -264,7 +262,7 @@ Java_com_intel_oap_mllib_regression_LinearRegressionDALImpl_cLinearRegressionTra
jlong featureCols, jlong label, jlong labelCols, jboolean fitIntercept,
jdouble regParam, jdouble elasticNetParam, jint executorNum,
jint executorCores, jint computeDeviceOrdinal, jintArray gpuIdxArray,
jobject resultObj) {
jstring ip_port, jobject resultObj) {

logger::println(logger::INFO,
"oneDAL (native): use DPC++ kernels; device %s",
Expand All @@ -279,19 +277,18 @@ Java_com_intel_oap_mllib_regression_LinearRegressionDALImpl_cLinearRegressionTra
jlong resultptr = 0L;
if (useGPU) {
#ifdef CPU_GPU_PROFILE
int nGpu = env->GetArrayLength(gpuIdxArray);
logger::println(
logger::INFO,
"oneDAL (native): use GPU kernels with %d GPU(s) rankid %d", nGpu,
rank);
"oneDAL (native): use GPU kernels with rankid %d", rank);

jint *gpuIndices = env->GetIntArrayElements(gpuIdxArray, 0);
auto queue = getAssignedGPU(device, gpuIndices);
const char *str = env->GetStringUTFChars(ip_port, nullptr);
ccl::string ccl_ip_port(str);
auto comm = createDalCommunicator(executorNum, rank, ccl_ip_port);

resultptr = doLROneAPICompute(env, rank, queue, feature, featureRows,
featureCols, label, labelCols,
fitIntercept, executorNum, resultObj);
env->ReleaseIntArrayElements(gpuIdxArray, gpuIndices, 0);
resultptr = doLROneAPICompute(
env, rank, comm, feature, featureRows, featureCols,
label, labelCols, fitIntercept, executorNum, resultObj);
env->ReleaseStringUTFChars(ip_port, str);
#endif
} else {
ccl::communicator &cclComm = getComm();
Expand Down
16 changes: 7 additions & 9 deletions mllib-dal/src/main/native/OneCCL.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -55,34 +55,32 @@ JNIEXPORT jint JNICALL Java_com_intel_oap_mllib_OneCCL_00024_c_1init(
auto t1 = std::chrono::high_resolution_clock::now();

ccl::init();
auto t2 = std::chrono::high_resolution_clock::now();


const char *str = env->GetStringUTFChars(ip_port, 0);
ccl::string ccl_ip_port(str);
const char *device = env->GetStringUTFChars(use_device, 0);
ccl::string ccl_ip_port(str);

auto &singletonCCLInit = CCLInitSingleton::get(size, rank, ccl_ip_port);

g_kvs.push_back(singletonCCLInit.kvs);

#ifdef CPU_ONLY_PROFILE
g_comms.push_back(
ccl::create_communicator(size, rank, singletonCCLInit.kvs));

auto t2 = std::chrono::high_resolution_clock::now();
auto duration =
(float)std::chrono::duration_cast<std::chrono::milliseconds>(t2 - t1)
.count();
logger::println(logger::INFO, "OneCCL (native): init took %f secs",
duration / 1000);
#endif

rank_id = getComm().rank();
comm_size = getComm().size();

jclass cls = env->GetObjectClass(param);
jfieldID fid_comm_size = env->GetFieldID(cls, "commSize", "J");
jfieldID fid_rank_id = env->GetFieldID(cls, "rankId", "J");

env->SetLongField(param, size, comm_size);
env->SetLongField(param, rank, rank_id);
env->SetLongField(param, fid_comm_size, comm_size);
env->SetLongField(param, fid_rank_id, rank_id);
env->ReleaseStringUTFChars(ip_port, str);

return 1;
Expand Down
22 changes: 8 additions & 14 deletions mllib-dal/src/main/native/PCAImpl.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -250,9 +250,9 @@ static void doPCAOneAPICompute(

JNIEXPORT jlong JNICALL
Java_com_intel_oap_mllib_feature_PCADALImpl_cPCATrainDAL(
JNIEnv *env, jobject obj, jint rank, jlong pNumTabData, jlong numRows,
jlong numCols, jint executorNum, jint executorCores,
jint computeDeviceOrdinal, jintArray gpuIdxArray, jobject resultObj) {
JNIEnv *env, jobject obj, jint rank, jlong pNumTabData, jlong numRows, jlong numCols,
jint executorNum, jint executorCores, jint computeDeviceOrdinal,
jintArray gpuIdxArray, jstring ip_port, jobject resultObj) {
logger::println(logger::INFO,
"oneDAL (native): use DPC++ kernels; device %s",
ComputeDeviceString[computeDeviceOrdinal].c_str());
Expand All @@ -277,22 +277,16 @@ Java_com_intel_oap_mllib_feature_PCADALImpl_cPCATrainDAL(
}
#ifdef CPU_GPU_PROFILE
case ComputeDevice::gpu: {
int nGpu = env->GetArrayLength(gpuIdxArray);
logger::println(
logger::INFO,
"oneDAL (native): use GPU kernels with %d GPU(s) rankid %d", nGpu,
rank);
"oneDAL (native): use GPU kernels with rankid %d", rank);

jint *gpuIndices = env->GetIntArrayElements(gpuIdxArray, 0);
const char *str = env->GetStringUTFChars(ip_port, nullptr);
ccl::string ccl_ip_port(str);
auto comm = createDalCommunicator(executorNum, rank, ccl_ip_port);

auto queue = getAssignedGPU(device, gpuIndices);

ccl::shared_ptr_class<ccl::kvs> &kvs = getKvs();
auto comm =
preview::spmd::make_communicator<preview::spmd::backend::ccl>(
queue, executorNum, rank, kvs);
doPCAOneAPICompute(env, pNumTabData, numRows, numCols, comm, resultObj);
env->ReleaseIntArrayElements(gpuIdxArray, gpuIndices, 0);
env->ReleaseStringUTFChars(ip_port, str);
break;
}
#endif
Expand Down
27 changes: 7 additions & 20 deletions mllib-dal/src/main/native/SummarizerImpl.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -268,15 +268,9 @@ static void doSummarizerOneAPICompute(

JNIEXPORT jlong JNICALL
Java_com_intel_oap_mllib_stat_SummarizerDALImpl_cSummarizerTrainDAL(
<<<<<<< HEAD
JNIEnv *env, jobject obj, jint rank, jlong pNumTabData, jlong numRows,
jlong numCols, jint executorNum, jint executorCores,
jint computeDeviceOrdinal, jintArray gpuIdxArray, jobject resultObj) {
=======
JNIEnv *env, jobject obj, jint rank, jlong pNumTabData, jlong numRows, jlong numCols,
jint executorNum, jint executorCores, jint computeDeviceOrdinal,
jintArray gpuIdxArray, jobject resultObj) {
>>>>>>> remove oneccl communicator
jintArray gpuIdxArray, jstring ip_port, jobject resultObj) {
logger::println(logger::INFO,
"oneDAL (native): use DPC++ kernels; device %s",
ComputeDeviceString[computeDeviceOrdinal].c_str());
Expand All @@ -301,28 +295,21 @@ Java_com_intel_oap_mllib_stat_SummarizerDALImpl_cSummarizerTrainDAL(
}
#ifdef CPU_GPU_PROFILE
case ComputeDevice::gpu: {
int nGpu = env->GetArrayLength(gpuIdxArray);
logger::println(
logger::INFO,
"oneDAL (native): use GPU kernels with %d GPU(s) rankid %d", nGpu,
rank);
"oneDAL (native): use GPU kernels with rankid %d", rank);
const char *str = env->GetStringUTFChars(ip_port, nullptr);
ccl::string ccl_ip_port(str);
auto comm = createDalCommunicator(executorNum, rank, ccl_ip_port);

jint *gpuIndices = env->GetIntArrayElements(gpuIdxArray, 0);
auto queue = getAssignedGPU(device, gpuIndices);

ccl::shared_ptr_class<ccl::kvs> &kvs = getKvs();
auto comm =
preview::spmd::make_communicator<preview::spmd::backend::ccl>(
queue, executorNum, rank, kvs);
doSummarizerOneAPICompute(env, pNumTabData, numRows, numCols, comm,
resultObj);
env->ReleaseIntArrayElements(gpuIdxArray, gpuIndices, 0);
env->ReleaseStringUTFChars(ip_port, str);
break;
}
#endif
default: {
deviceError("Summarizer",
ComputeDeviceString[computeDeviceOrdinal].c_str());
deviceError("Summarizer", ComputeDeviceString[computeDeviceOrdinal].c_str());
}
}
return 0;
Expand Down
2 changes: 1 addition & 1 deletion mllib-dal/src/main/native/javah/com_intel_oap_mllib_OneCCL__.h
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2 changes: 1 addition & 1 deletion mllib-dal/src/main/native/javah/com_intel_oap_mllib_clustering_KMeansDALImpl.h
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2 changes: 1 addition & 1 deletion mllib-dal/src/main/native/javah/com_intel_oap_mllib_feature_PCADALImpl.h
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