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Commit 3e16cab9 authored by Peter Eastman's avatar Peter Eastman
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Continuing to implement new CUDA platform

parent abb8cb4b
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platforms/cuda2/src/CudaArray.h
View file @ 3e16cab9
......@@ -83,7 +83,7 @@ public:
/**
* Get a pointer to the device memory.
*/
CUdeviceptr getDevicePointer() {
CUdeviceptr& getDevicePointer() {
return pointer;
}
/**
......
platforms/cuda2/src/CudaContext.cpp
View file @ 3e16cab9
This diff is collapsed.
platforms/cuda2/src/CudaContext.h
View file @ 3e16cab9
......@@ -72,11 +72,11 @@ public:
CudaContext(const System& system, int deviceIndex, bool useBlockingSync, const std::string& precision,
const std::string& compiler, const std::string& tempDir, CudaPlatform::PlatformData& platformData);
~CudaContext();
// /**
// * This is called to initialize internal data structures after all Forces in the system
// * have been initialized.
// */
// void initialize();
/**
* This is called to initialize internal data structures after all Forces in the system
* have been initialized.
*/
void initialize();
/**
* Add a CudaForce to this context.
*/
......@@ -123,12 +123,12 @@ public:
CudaArray& getVelm() {
return *velm;
}
// /**
// * Get the array which contains the force on each atom.
// */
// CudaArray<mm_float4>& getForce() {
// return *force;
// }
/**
* Get the array which contains the force on each atom (respresented as a long3 in 64 bit fixed point).
*/
CudaArray& getForce() {
return *force;
}
// /**
// * Get the array which contains the buffers in which forces are computed.
// */
......@@ -184,36 +184,41 @@ public:
* omitted, a default set of options will be used
*/
CUmodule createModule(const std::string source, const std::map<std::string, std::string>& defines, const char* optimizationFlags = NULL);
// /**
// * Execute a kernel.
// *
// * @param kernel the kernel to execute
// * @param workUnits the maximum number of work units that should be used
// * @param blockSize the size of each thread block to use
// */
// void executeKernel(cl::Kernel& kernel, int workUnits, int blockSize = -1);
// /**
// * Set all elements of an array to 0.
// */
// void clearBuffer(CudaArray<float>& array);
// /**
// * Set all elements of an array to 0.
// */
// void clearBuffer(CudaArray<mm_float4>& array);
// /**
// * Set all elements of an array to 0.
// *
// * @param memory the Memory to clear
// * @param size the number of float elements in the buffer
// */
// void clearBuffer(cl::Memory& memory, int size);
// /**
// * Register a buffer that should be automatically cleared (all elements set to 0) at the start of each force or energy computation.
// *
// * @param memory the Memory to clear
// * @param size the number of float elements in the buffer
// */
// void addAutoclearBuffer(cl::Memory& memory, int size);
/**
* Get a kernel from a CUDA module.
*
* @param module the module to get the kernel from
* @param name the name of the kernel to get
*/
CUfunction getKernel(CUmodule& module, const std::string& name);
/**
* Execute a kernel.
*
* @param kernel the kernel to execute
* @param arguments an array of pointers to the kernel arguments
* @param threads the maximum number of threads that should be used
* @param blockSize the size of each thread block to use
* @param sharedSize the amount of dynamic shared memory to allocated for the kernel, in bytes
*/
void executeKernel(CUfunction kernel, void** arguments, int workUnits, int blockSize = -1, unsigned int sharedSize = 0);
/**
* Set all elements of an array to 0.
*/
void clearBuffer(CudaArray& array);
/**
* Set all elements of an array to 0.
*
* @param memory the memory to clear
* @param size the number of 4-byte elements in the buffer
*/
void clearBuffer(CUdeviceptr memory, int size);
/**
* Register a buffer that should be automatically cleared (all elements set to 0) at the start of each force or energy computation.
*
* @param memory the memory to clear
* @param size the number of float/double elements in the buffer
*/
void addAutoclearBuffer(CUdeviceptr memory, int size);
// /**
// * Clear all buffers that have been registered with addAutoclearBuffer().
// */
......@@ -230,108 +235,110 @@ public:
// * Sum the buffesr containing forces.
// */
// void reduceForces();
// /**
// * Get the current simulation time.
// */
// double getTime() {
// return time;
// }
// /**
// * Set the current simulation time.
// */
// void setTime(double t) {
// time = t;
// }
// /**
// * Get the number of integration steps that have been taken.
// */
// int getStepCount() {
// return stepCount;
// }
// /**
// * Set the number of integration steps that have been taken.
// */
// void setStepCount(int steps) {
// stepCount = steps;
// }
// /**
// * Get the number of times forces or energy has been computed.
// */
// int getComputeForceCount() {
// return computeForceCount;
// }
// /**
// * Set the number of times forces or energy has been computed.
// */
// void setComputeForceCount(int count) {
// computeForceCount = count;
// }
// /**
// * Get the number of atoms.
// */
// int getNumAtoms() const {
// return numAtoms;
// }
// /**
// * Get the number of atoms, rounded up to a multiple of TileSize. This is the actual size of
// * most arrays with one element per atom.
// */
// int getPaddedNumAtoms() const {
// return paddedNumAtoms;
// }
// /**
// * Get the number of blocks of TileSize atoms.
// */
// int getNumAtomBlocks() const {
// return numAtomBlocks;
// }
// /**
// * Get the standard number of thread blocks to use when executing kernels.
// */
// int getNumThreadBlocks() const {
// return numThreadBlocks;
// }
// /**
// * Get the number of force buffers.
// */
// int getNumForceBuffers() const {
// return numForceBuffers;
// }
// /**
// * Get the SIMD width of the device being used.
// */
// int getSIMDWidth() const {
// return simdWidth;
// }
// /**
// * Get whether the device being used supports 64 bit atomic operations on global memory.
// */
// bool getSupports64BitGlobalAtomics() {
// return supports64BitGlobalAtomics;
// }
// /**
// * Get whether the device being used supports double precision math.
// */
// bool getSupportsDoublePrecision() {
// return supportsDoublePrecision;
// }
/**
* Get the current simulation time.
*/
double getTime() {
return time;
}
/**
* Set the current simulation time.
*/
void setTime(double t) {
time = t;
}
/**
* Get the number of integration steps that have been taken.
*/
int getStepCount() {
return stepCount;
}
/**
* Set the number of integration steps that have been taken.
*/
void setStepCount(int steps) {
stepCount = steps;
}
/**
* Get the number of times forces or energy has been computed.
*/
int getComputeForceCount() {
return computeForceCount;
}
/**
* Set the number of times forces or energy has been computed.
*/
void setComputeForceCount(int count) {
computeForceCount = count;
}
/**
* Get the number of atoms.
*/
int getNumAtoms() const {
return numAtoms;
}
/**
* Get the number of atoms, rounded up to a multiple of TileSize. This is the actual size of
* most arrays with one element per atom.
*/
int getPaddedNumAtoms() const {
return paddedNumAtoms;
}
/**
* Get the number of blocks of TileSize atoms.
*/
int getNumAtomBlocks() const {
return numAtomBlocks;
}
/**
* Get the standard number of thread blocks to use when executing kernels.
*/
int getNumThreadBlocks() const {
return numThreadBlocks;
}
/**
* Get whether double precision is being used.
*/
bool getUseDoublePrecision() {
return useDoublePrecision;
}
/**
* Get whether accumulation is being done in double precision.
*/
bool getAccumulateInDouble() {
return accumulateInDouble;
}
/**
* Convert a number to a string in a format suitable for including in a kernel.
* This takes into account whether the context uses single or double precision.
*/
std::string doubleToString(double value);
/**
* Convert a number to a string in a format suitable for including in a kernel.
*/
std::string intToString(int value);
/**
* Convert a CUDA result code to the corresponding string description.
*/
std::string getErrorString(CUresult result);
// /**
// * Get the size of the periodic box.
// */
// mm_float4 getPeriodicBoxSize() const {
// float4 getPeriodicBoxSize() const {
// return periodicBoxSize;
// }
// /**
// * Set the size of the periodic box.
// */
// void setPeriodicBoxSize(double xsize, double ysize, double zsize) {
// periodicBoxSize = mm_float4((float) xsize, (float) ysize, (float) zsize, 0);
// invPeriodicBoxSize = mm_float4((float) (1.0/xsize), (float) (1.0/ysize), (float) (1.0/zsize), 0);
// periodicBoxSize = make_float4((float) xsize, (float) ysize, (float) zsize, 0);
// invPeriodicBoxSize = make_float4((float) (1.0/xsize), (float) (1.0/ysize), (float) (1.0/zsize), 0);
// }
// /**
// * Get the inverse of the size of the periodic box.
// */
// mm_float4 getInvPeriodicBoxSize() const {
// float4 getInvPeriodicBoxSize() const {
// return invPeriodicBoxSize;
// }
// /**
......@@ -352,66 +359,66 @@ public:
// CudaNonbondedUtilities& getNonbondedUtilities() {
// return *nonbonded;
// }
// /**
// * Get the thread used by this context for executing parallel computations.
// */
// WorkThread& getWorkThread() {
// return *thread;
// }
// /**
// * Get whether atoms were reordered during the most recent force/energy computation.
// */
// bool getAtomsWereReordered() const {
// return atomsWereReordered;
// }
// /**
// * Set whether atoms were reordered during the most recent force/energy computation.
// */
// void setAtomsWereReordered(bool wereReordered) {
// atomsWereReordered = wereReordered;
// }
// /**
// * Reorder the internal arrays of atoms to try to keep spatially contiguous atoms close
// * together in the arrays.
// *
// * @param enforcePeriodic if true, the atom positions may be altered to enforce periodic boundary conditions
// */
// void reorderAtoms(bool enforcePeriodic);
// /**
// * Add a listener that should be called whenever atoms get reordered. The CudaContext
// * assumes ownership of the object, and deletes it when the context itself is deleted.
// */
// void addReorderListener(ReorderListener* listener);
// /**
// * Get the list of ReorderListeners.
// */
// std::vector<ReorderListener*>& getReorderListeners() {
// return reorderListeners;
// }
// /**
// * Mark that the current molecule definitions (and hence the atom order) may be invalid.
// * This should be called whenever force field parameters change. It will cause the definitions
// * and order to be revalidated the next to reorderAtoms() is called.
// */
// void invalidateMolecules();
// /**
// * Get whether the current molecule definitions are valid.
// */
// bool getMoleculesAreInvalid() {
// return moleculesInvalid;
// }
/**
* Get the thread used by this context for executing parallel computations.
*/
WorkThread& getWorkThread() {
return *thread;
}
/**
* Get whether atoms were reordered during the most recent force/energy computation.
*/
bool getAtomsWereReordered() const {
return atomsWereReordered;
}
/**
* Set whether atoms were reordered during the most recent force/energy computation.
*/
void setAtomsWereReordered(bool wereReordered) {
atomsWereReordered = wereReordered;
}
/**
* Reorder the internal arrays of atoms to try to keep spatially contiguous atoms close
* together in the arrays.
*
* @param enforcePeriodic if true, the atom positions may be altered to enforce periodic boundary conditions
*/
void reorderAtoms(bool enforcePeriodic);
/**
* Add a listener that should be called whenever atoms get reordered. The CudaContext
* assumes ownership of the object, and deletes it when the context itself is deleted.
*/
void addReorderListener(ReorderListener* listener);
/**
* Get the list of ReorderListeners.
*/
std::vector<ReorderListener*>& getReorderListeners() {
return reorderListeners;
}
/**
* Mark that the current molecule definitions (and hence the atom order) may be invalid.
* This should be called whenever force field parameters change. It will cause the definitions
* and order to be revalidated the next to reorderAtoms() is called.
*/
void invalidateMolecules();
/**
* Get whether the current molecule definitions are valid.
*/
bool getMoleculesAreInvalid() {
return moleculesInvalid;
}
private:
struct Molecule;
struct MoleculeGroup;
class VirtualSiteInfo;
// void findMoleculeGroups();
// static void tagAtomsInMolecule(int atom, int molecule, std::vector<int>& atomMolecule, std::vector<std::vector<int> >& atomBonds);
// /**
// * Ensure that all molecules marked as "identical" really are identical. This should be
// * called whenever force field parameters change. If necessary, it will rebuild the list
// * of molecules and resort the atoms.
// */
// void validateMolecules();
void findMoleculeGroups();
static void tagAtomsInMolecule(int atom, int molecule, std::vector<int>& atomMolecule, std::vector<std::vector<int> >& atomBonds);
/**
* Ensure that all molecules marked as "identical" really are identical. This should be
* called whenever force field parameters change. If necessary, it will rebuild the list
* of molecules and resort the atoms.
*/
void validateMolecules();
static bool hasInitializedCuda;
const System& system;
double time;
......@@ -424,8 +431,6 @@ private:
int paddedNumAtoms;
int numAtomBlocks;
int numThreadBlocks;
// int numForceBuffers;
// int simdWidth;
bool useBlockingSync, useDoublePrecision, accumulateInDouble, contextIsValid, atomsWereReordered, moleculesInvalid;
std::string compiler, tempDir, gpuArchitecture;
float4 periodicBoxSize;
......@@ -446,15 +451,15 @@ private:
std::vector<Molecule> molecules;
std::vector<MoleculeGroup> moleculeGroups;
std::vector<int4> posCellOffsets;
void* pinnedBuffer;
CudaArray* posq;
CudaArray* velm;
// CudaArray<mm_float4>* force;
// CudaArray<mm_float4>* forceBuffers;
// CudaArray<cl_long>* longForceBuffer;
// CudaArray<cl_float>* energyBuffer;
// CudaArray<cl_int>* atomIndex;
// std::vector<cl::Memory*> autoclearBuffers;
// std::vector<int> autoclearBufferSizes;
CudaArray* force;
CudaArray* energyBuffer;
CudaArray* atomIndexDevice;
std::vector<int> atomIndex;
std::vector<CUdeviceptr> autoclearBuffers;
std::vector<int> autoclearBufferSizes;
std::vector<ReorderListener*> reorderListeners;
// CudaIntegrationUtilities* integration;
// CudaBondedUtilities* bonded;
......
platforms/cuda2/src/CudaPlatform.cpp
View file @ 3e16cab9
......@@ -154,6 +154,7 @@ CudaPlatform::PlatformData::PlatformData(const System& system, const string& dev
device << contexts[i]->getDeviceIndex();
}
propertyValues[CudaPlatform::CudaDeviceIndex()] = device.str();
propertyValues[CudaPlatform::CudaUseBlockingSync()] = blocking ? "true" : "false";
propertyValues[CudaPlatform::CudaPrecision()] = precisionProperty;
propertyValues[CudaPlatform::CudaCompiler()] = compilerProperty;
propertyValues[CudaPlatform::CudaTempDirectory()] = tempProperty;
......@@ -166,11 +167,11 @@ CudaPlatform::PlatformData::~PlatformData() {
}
void CudaPlatform::PlatformData::initializeContexts(const System& system) {
// for (int i = 0; i < (int) contexts.size(); i++)
// contexts[i]->initialize();
for (int i = 0; i < (int) contexts.size(); i++)
contexts[i]->initialize();
}
void CudaPlatform::PlatformData::syncContexts() {
// for (int i = 0; i < (int) contexts.size(); i++)
// contexts[i]->getWorkThread().flush();
for (int i = 0; i < (int) contexts.size(); i++)
contexts[i]->getWorkThread().flush();
}
platforms/cuda2/src/CudaSort.cpp 0 → 100644
View file @ 3e16cab9
/* -------------------------------------------------------------------------- *
* OpenMM *
* -------------------------------------------------------------------------- *
* This is part of the OpenMM molecular simulation toolkit originating from *
* Simbios, the NIH National Center for Physics-Based Simulation of *
* Biological Structures at Stanford, funded under the NIH Roadmap for *
* Medical Research, grant U54 GM072970. See https://simtk.org. *
* *
* Portions copyright (c) 2010-2012 Stanford University and the Authors. *
* Authors: Peter Eastman *
* Contributors: *
* *
* This program is free software: you can redistribute it and/or modify *
* it under the terms of the GNU Lesser General Public License as published *
* by the Free Software Foundation, either version 3 of the License, or *
* (at your option) any later version. *
* *
* This program is distributed in the hope that it will be useful, *
* but WITHOUT ANY WARRANTY; without even the implied warranty of *
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the *
* GNU Lesser General Public License for more details. *
* *
* You should have received a copy of the GNU Lesser General Public License *
* along with this program. If not, see <http://www.gnu.org/licenses/>. *
* -------------------------------------------------------------------------- */
#include "CudaSort.h"
#include "CudaKernelSources.h"
#include <map>
using namespace OpenMM;
using namespace std;
CudaSort::CudaSort(CudaContext& context, SortTrait* trait, unsigned int length) : context(context), trait(trait),
dataRange(NULL), bucketOfElement(NULL), offsetInBucket(NULL), bucketOffset(NULL), buckets(NULL) {
// Create kernels.
map<string, string> replacements;
replacements["DATA_TYPE"] = trait->getDataType();
replacements["KEY_TYPE"] = trait->getKeyType();
replacements["SORT_KEY"] = trait->getSortKey();
replacements["MIN_KEY"] = trait->getMinKey();
replacements["MAX_KEY"] = trait->getMaxKey();
replacements["MAX_VALUE"] = trait->getMaxValue();
CUmodule module = context.createModule(context.replaceStrings(CudaKernelSources::sort, replacements));
computeRangeKernel = context.getKernel(module, "computeRange");
assignElementsKernel = context.getKernel(module, "assignElementsToBuckets");
computeBucketPositionsKernel = context.getKernel(module, "computeBucketPositions");
copyToBucketsKernel = context.getKernel(module, "copyDataToBuckets");
sortBucketsKernel = context.getKernel(module, "sortBuckets");
// Work out the work group sizes for various kernels.
int maxBlockSize;
cuDeviceGetAttribute(&maxBlockSize, CU_DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X, context.getDevice());
for (rangeKernelSize = 1; rangeKernelSize*2 <= maxBlockSize; rangeKernelSize *= 2)
;
positionsKernelSize = rangeKernelSize;
sortKernelSize = rangeKernelSize/2;
if (rangeKernelSize > length)
rangeKernelSize = length;
int maxSharedMem;
cuDeviceGetAttribute(&maxSharedMem, CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK, context.getDevice());
unsigned int maxLocalBuffer = (unsigned int) ((maxSharedMem/trait->getDataSize())/2);
if (sortKernelSize > maxLocalBuffer)
sortKernelSize = maxLocalBuffer;
unsigned int targetBucketSize = sortKernelSize/2;
unsigned int numBuckets = length/targetBucketSize;
if (numBuckets < 1)
numBuckets = 1;
if (positionsKernelSize > numBuckets)
positionsKernelSize = numBuckets;
// Create workspace arrays.
dataRange = new CudaArray(2, trait->getKeySize(), "sortDataRange");
bucketOffset = CudaArray::create<uint1>(numBuckets, "bucketOffset");
bucketOfElement = CudaArray::create<uint1>(length, "bucketOfElement");
offsetInBucket = CudaArray::create<uint1>(length, "offsetInBucket");
buckets = new CudaArray(length, trait->getDataSize(), "buckets");
}
CudaSort::~CudaSort() {
delete trait;
if (dataRange != NULL)
delete dataRange;
if (bucketOfElement != NULL)
delete bucketOfElement;
if (offsetInBucket != NULL)
delete offsetInBucket;
if (bucketOffset != NULL)
delete bucketOffset;
if (buckets != NULL)
delete buckets;
}
void CudaSort::sort(CudaArray& data) {
if (data.getSize() != bucketOfElement->getSize() || data.getElementSize() != trait->getDataSize())
throw OpenMMException("CudaSort called with different data size");
if (data.getSize() == 0)
return;
// Compute the range of data values.
unsigned int dataSize = data.getSize();
void* rangeArgs[] = {&data.getDevicePointer(), &dataSize, &dataRange->getDevicePointer()};
context.executeKernel(computeRangeKernel, rangeArgs, rangeKernelSize, rangeKernelSize, rangeKernelSize*trait->getKeySize());
// Assign array elements to buckets.
unsigned int numBuckets = bucketOffset->getSize();
context.clearBuffer(*bucketOffset);
void* elementsArgs[] = {&data.getDevicePointer(), &dataSize, &numBuckets, &dataRange->getDevicePointer(),
&bucketOffset->getDevicePointer(), &bucketOfElement->getDevicePointer(), &offsetInBucket->getDevicePointer()};
context.executeKernel(assignElementsKernel, elementsArgs, data.getSize());
// Compute the position of each bucket.
void* computeArgs[] = {&numBuckets, &bucketOffset->getDevicePointer()};
context.executeKernel(computeBucketPositionsKernel, computeArgs, positionsKernelSize, positionsKernelSize, positionsKernelSize*sizeof(int));
// Copy the data into the buckets.
void* copyArgs[] = {&data.getDevicePointer(), &buckets->getDevicePointer(), &dataSize, &bucketOffset->getDevicePointer(),
&bucketOfElement->getDevicePointer(), &offsetInBucket->getDevicePointer()};
context.executeKernel(copyToBucketsKernel, copyArgs, data.getSize());
// Sort each bucket.
void* sortArgs[] = {&data.getDevicePointer(), &buckets->getDevicePointer(), &numBuckets, &bucketOffset->getDevicePointer()};
context.executeKernel(sortBucketsKernel, sortArgs, ((data.getSize()+sortKernelSize-1)/sortKernelSize)*sortKernelSize, sortKernelSize, sortKernelSize*trait->getDataSize());
}
platforms/cuda2/src/CudaSort.h 0 → 100644
View file @ 3e16cab9
#ifndef __OPENMM_CUDASORT_H__
#define __OPENMM_CUDASORT_H__
/* -------------------------------------------------------------------------- *
* OpenMM *
* -------------------------------------------------------------------------- *
* This is part of the OpenMM molecular simulation toolkit originating from *
* Simbios, the NIH National Center for Physics-Based Simulation of *
* Biological Structures at Stanford, funded under the NIH Roadmap for *
* Medical Research, grant U54 GM072970. See https://simtk.org. *
* *
* Portions copyright (c) 2010-2012 Stanford University and the Authors. *
* Authors: Peter Eastman *
* Contributors: *
* *
* This program is free software: you can redistribute it and/or modify *
* it under the terms of the GNU Lesser General Public License as published *
* by the Free Software Foundation, either version 3 of the License, or *
* (at your option) any later version. *
* *
* This program is distributed in the hope that it will be useful, *
* but WITHOUT ANY WARRANTY; without even the implied warranty of *
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the *
* GNU Lesser General Public License for more details. *
* *
* You should have received a copy of the GNU Lesser General Public License *
* along with this program. If not, see <http://www.gnu.org/licenses/>. *
* -------------------------------------------------------------------------- */
#include "CudaArray.h"
#include "openmm/internal/windowsExport.h"
#include "CudaContext.h"
namespace OpenMM {
/**
* This class sorts arrays of values. It supports any type of values, not just scalars,
* so long as an appropriate sorting key can be defined by which to sort them.
*
* The sorting behavior is specified by a "trait" class that defines the type of data to
* sort and the key for sorting it. Here is an example of a trait class for
* sorting floats:
*
* class SortTrait : public CudaSort::SortTrait {
* int getDataSize() const {return 4;}
* int getKeySize() const {return 4;}
* const char* getDataType() const {return "float";}
* const char* getKeyType() const {return "float";}
* const char* getMinKey() const {return "-MAXFLOAT";}
* const char* getMaxKey() const {return "MAXFLOAT";}
* const char* getMaxValue() const {return "MAXFLOAT";}
* const char* getSortKey() const {return "value";}
* };
*
* The algorithm used is a bucket sort, followed by a bitonic sort within each bucket
* (in local memory when possible, in global memory otherwise). This is similar to
* the algorithm described in
*
* Shifu Chen, Jing Qin, Yongming Xie, Junping Zhao, and Pheng-Ann Heng. "An Efficient
* Sorting Algorithm with CUDA" Journal of the Chinese Institute of Engineers, 32(7),
* pp. 915-921 (2009)
*
* but with many modifications and simplifications. In particular, this algorithm
* involves much less communication between host and device, which is critical to get
* good performance with the array sizes we typically work with (10,000 to 100,000
* elements).
*/
class OPENMM_EXPORT CudaSort {
public:
class SortTrait;
/**
* Create a CudaSort object for sorting data of a particular type.
*
* @param context the context in which to perform calculations
* @param trait a SortTrait defining the type of data to sort. It should have been allocated
* on the heap with the "new" operator. This object takes over ownership of it,
* and deletes it when the CudaSort is deleted.
* @param length the length of the arrays this object will be used to sort
*/
CudaSort(CudaContext& context, SortTrait* trait, unsigned int length);
~CudaSort();
/**
* Sort an array.
*/
void sort(CudaArray& data);
private:
CudaContext& context;
SortTrait* trait;
CudaArray* dataRange;
CudaArray* bucketOfElement;
CudaArray* offsetInBucket;
CudaArray* bucketOffset;
CudaArray* buckets;
CUfunction computeRangeKernel, assignElementsKernel, computeBucketPositionsKernel, copyToBucketsKernel, sortBucketsKernel;
unsigned int rangeKernelSize, positionsKernelSize, sortKernelSize;
};
/**
* A subclass of SortTrait defines the type of value to sort, and the key for sorting them.
*/
class CudaSort::SortTrait {
public:
/**
* Get the size of each data value in bytes.
*/
virtual int getDataSize() const = 0;
/**
* Get the size of each key value in bytes.
*/
virtual int getKeySize() const = 0;
/**
* Get the data type of the values to sort.
*/
virtual const char* getDataType() const = 0;
/**
* Get the data type of the sorting key.
*/
virtual const char* getKeyType() const = 0;
/**
* Get the minimum value a key can take.
*/
virtual const char* getMinKey() const = 0;
/**
* Get the maximum value a key can take.
*/
virtual const char* getMaxKey() const = 0;
/**
* Get a value whose key is guaranteed to equal getMaxKey().
*/
virtual const char* getMaxValue() const = 0;
/**
* Get the CUDA code to select the key from the data value.
*/
virtual const char* getSortKey() const = 0;
};
} // namespace OpenMM
#endif // __OPENMM_CUDASORT_H__
platforms/cuda2/src/kernels/sort.cu 0 → 100644
View file @ 3e16cab9
__device__ KEY_TYPE getValue(DATA_TYPE value) {
return SORT_KEY;
}
extern "C" {
/**
* Calculate the minimum and maximum value in the array to be sorted. This kernel
* is executed as a single work group.
*/
__global__ void computeRange(const DATA_TYPE* __restrict__ data, unsigned int length, KEY_TYPE* __restrict__ range) {
extern __shared__ KEY_TYPE rangeBuffer[];
KEY_TYPE minimum = MAX_KEY;
KEY_TYPE maximum = MIN_KEY;
// Each thread calculates the range of a subset of values.
for (unsigned int index = threadIdx.x; index < length; index += blockDim.x) {
KEY_TYPE value = getValue(data[index]);
minimum = min(minimum, value);
maximum = max(maximum, value);
}
// Now reduce them.
rangeBuffer[threadIdx.x] = minimum;
__syncthreads();
for (unsigned int step = 1; step < blockDim.x; step *= 2) {
if (threadIdx.x+step < blockDim.x && threadIdx.x%(2*step) == 0)
rangeBuffer[threadIdx.x] = min(rangeBuffer[threadIdx.x], rangeBuffer[threadIdx.x+step]);
__syncthreads();
}
minimum = rangeBuffer[0];
rangeBuffer[threadIdx.x] = maximum;
__syncthreads();
for (unsigned int step = 1; step < blockDim.x; step *= 2) {
if (threadIdx.x+step < blockDim.x && threadIdx.x%(2*step) == 0)
rangeBuffer[threadIdx.x] = max(rangeBuffer[threadIdx.x], rangeBuffer[threadIdx.x+step]);
__syncthreads();
}
maximum = rangeBuffer[0];
if (threadIdx.x == 0) {
range[0] = minimum;
range[1] = maximum;
}
}
/**
* Assign elements to buckets.
*/
__global__ void assignElementsToBuckets(const DATA_TYPE* __restrict__ data, unsigned int length, unsigned int numBuckets, const KEY_TYPE* __restrict__ range,
unsigned int* bucketOffset, unsigned int* __restrict__ bucketOfElement, unsigned int* __restrict__ offsetInBucket) {
float minValue = (float) (range[0]);
float maxValue = (float) (range[1]);
float bucketWidth = (maxValue-minValue)/numBuckets;
for (unsigned int index = blockDim.x*blockIdx.x+threadIdx.x; index < length; index += blockDim.x*gridDim.x) {
float key = (float) getValue(data[index]);
unsigned int bucketIndex = min((unsigned int) ((key-minValue)/bucketWidth), numBuckets-1);
offsetInBucket[index] = atomicAdd(&bucketOffset[bucketIndex], 1);
bucketOfElement[index] = bucketIndex;
}
}
/**
* Sum the bucket sizes to compute the start position of each bucket. This kernel
* is executed as a single work group.
*/
__global__ void computeBucketPositions(unsigned int numBuckets, unsigned int* __restrict__ bucketOffset) {
extern __shared__ unsigned int posBuffer[];
unsigned int globalOffset = 0;
for (unsigned int startBucket = 0; startBucket < numBuckets; startBucket += blockDim.x) {
// Load the bucket sizes into local memory.
unsigned int globalIndex = startBucket+threadIdx.x;
posBuffer[threadIdx.x] = (globalIndex < numBuckets ? bucketOffset[globalIndex] : 0);
__syncthreads();
// Perform a parallel prefix sum.
for (unsigned int step = 1; step < blockDim.x; step *= 2) {
unsigned int add = (threadIdx.x >= step ? posBuffer[threadIdx.x-step] : 0);
__syncthreads();
posBuffer[threadIdx.x] += add;
__syncthreads();
}
// Write the results back to global memory.
if (globalIndex < numBuckets)
bucketOffset[globalIndex] = posBuffer[threadIdx.x]+globalOffset;
globalOffset += posBuffer[blockDim.x-1];
}
}
/**
* Copy the input data into the buckets for sorting.
*/
__global__ void copyDataToBuckets(const DATA_TYPE* __restrict__ data, DATA_TYPE* __restrict__ buckets, unsigned int length, const unsigned int* __restrict__ bucketOffset, const unsigned int* __restrict__ bucketOfElement, const unsigned int* __restrict__ offsetInBucket) {
for (unsigned int index = blockDim.x*blockIdx.x+threadIdx.x; index < length; index += blockDim.x*gridDim.x) {
DATA_TYPE element = data[index];
unsigned int bucketIndex = bucketOfElement[index];
unsigned int offset = (bucketIndex == 0 ? 0 : bucketOffset[bucketIndex-1]);
buckets[offset+offsetInBucket[index]] = element;
}
}
/**
* Sort the data in each bucket.
*/
__global__ void sortBuckets(DATA_TYPE* __restrict__ data, const DATA_TYPE* __restrict__ buckets, unsigned int numBuckets, const unsigned int* __restrict__ bucketOffset) {
extern __shared__ DATA_TYPE dataBuffer[];
for (unsigned int index = blockIdx.x; index < numBuckets; index += gridDim.x) {
unsigned int startIndex = (index == 0 ? 0 : bucketOffset[index-1]);
unsigned int endIndex = bucketOffset[index];
unsigned int length = endIndex-startIndex;
if (length <= blockDim.x) {
// Load the data into local memory.
if (threadIdx.x < length)
dataBuffer[threadIdx.x] = buckets[startIndex+threadIdx.x];
else
dataBuffer[threadIdx.x] = MAX_VALUE;
__syncthreads();
// Perform a bitonic sort in local memory.
for (unsigned int k = 2; k <= blockDim.x; k *= 2) {
for (unsigned int j = k/2; j > 0; j /= 2) {
int ixj = threadIdx.x^j;
if (ixj > threadIdx.x) {
DATA_TYPE value1 = dataBuffer[threadIdx.x];
DATA_TYPE value2 = dataBuffer[ixj];
bool ascending = (threadIdx.x&k) == 0;
KEY_TYPE lowKey = (ascending ? getValue(value1) : getValue(value2));
KEY_TYPE highKey = (ascending ? getValue(value2) : getValue(value1));
if (lowKey > highKey) {
dataBuffer[threadIdx.x] = value2;
dataBuffer[ixj] = value1;
}
}
__syncthreads();
}
}
// Write the data to the sorted array.
if (threadIdx.x < length)
data[startIndex+threadIdx.x] = dataBuffer[threadIdx.x];
}
else {
// Copy the bucket data over to the output array.
for (unsigned int i = threadIdx.x; i < length; i += blockDim.x)
data[startIndex+i] = buckets[startIndex+i];
__threadfence_block();
__syncthreads();
// Perform a bitonic sort in global memory.
for (unsigned int k = 2; k < 2*length; k *= 2) {
for (unsigned int j = k/2; j > 0; j /= 2) {
for (unsigned int i = threadIdx.x; i < length; i += blockDim.x) {
int ixj = i^j;
if (ixj > i && ixj < length) {
DATA_TYPE value1 = data[startIndex+i];
DATA_TYPE value2 = data[startIndex+ixj];
bool ascending = ((i&k) == 0);
for (unsigned int mask = k*2; mask < 2*length; mask *= 2)
ascending = ((i&mask) == 0 ? !ascending : ascending);
KEY_TYPE lowKey = (ascending ? getValue(value1) : getValue(value2));
KEY_TYPE highKey = (ascending ? getValue(value2) : getValue(value1));
if (lowKey > highKey) {
data[startIndex+i] = value2;
data[startIndex+ixj] = value1;
}
}
}
__threadfence_block();
__syncthreads();
}
}
}
}
}
}
\ No newline at end of file
platforms/cuda2/src/kernels/utilities.cu
View file @ 3e16cab9
extern "C" {
/**
* This is called by the various functions below to clear a buffer.
*/
......@@ -100,3 +102,5 @@ __global__ void reduceForces(const long* __restrict__ longBuffer, float4* __rest
buffer[index] = sum;
}
}
}
\ No newline at end of file
platforms/cuda2/tests/TestCudaSort.cpp 0 → 100644
View file @ 3e16cab9
/* -------------------------------------------------------------------------- *
* OpenMM *
* -------------------------------------------------------------------------- *
* This is part of the OpenMM molecular simulation toolkit originating from *
* Simbios, the NIH National Center for Physics-Based Simulation of *
* Biological Structures at Stanford, funded under the NIH Roadmap for *
* Medical Research, grant U54 GM072970. See https://simtk.org. *
* *
* Portions copyright (c) 2008-2012 Stanford University and the Authors. *
* Authors: Peter Eastman *
* Contributors: *
* *
* Permission is hereby granted, free of charge, to any person obtaining a *
* copy of this software and associated documentation files (the "Software"), *
* to deal in the Software without restriction, including without limitation *
* the rights to use, copy, modify, merge, publish, distribute, sublicense, *
* and/or sell copies of the Software, and to permit persons to whom the *
* Software is furnished to do so, subject to the following conditions: *
* *
* The above copyright notice and this permission notice shall be included in *
* all copies or substantial portions of the Software. *
* *
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR *
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, *
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL *
* THE AUTHORS, CONTRIBUTORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, *
* DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR *
* OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE *
* USE OR OTHER DEALINGS IN THE SOFTWARE. *
* -------------------------------------------------------------------------- */
/**
* This tests the CUDA implementation of sorting.
*/
#include "openmm/internal/AssertionUtilities.h"
#include "../src/CudaArray.h"
#include "../src/CudaContext.h"
#include "../src/CudaSort.h"
#include "sfmt/SFMT.h"
#include "openmm/System.h"
#include <iostream>
#include <cmath>
#include <set>
using namespace OpenMM;
using namespace std;
class SortTrait : public CudaSort::SortTrait {
int getDataSize() const {return 4;}
int getKeySize() const {return 4;}
const char* getDataType() const {return "float";}
const char* getKeyType() const {return "float";}
const char* getMinKey() const {return "-MAXFLOAT";}
const char* getMaxKey() const {return "MAXFLOAT";}
const char* getMaxValue() const {return "MAXFLOAT";}
const char* getSortKey() const {return "value";}
};
void verifySorting(vector<float> array) {
// Sort the array.
System system;
system.addParticle(0.0);
CudaPlatform platform;
CudaPlatform::PlatformData platformData(system, "", "true", "single",
platform.getPropertyDefaultValue(CudaPlatform::CudaCompiler()), platform.getPropertyDefaultValue(CudaPlatform::CudaTempDirectory()));
CudaContext& context = *platformData.contexts[0];
context.initialize();
CudaArray data(array.size(), 4, "sortData");
data.upload(array);
CudaSort sort(context, new SortTrait(), array.size());
sort.sort(data);
vector<float> sorted;
data.download(sorted);
// Verify that it is in sorted order.
for (int i = 1; i < (int) sorted.size(); i++)
ASSERT(sorted[i-1] <= sorted[i]);
// Make sure the sorted array contains the same values as the original one.
multiset<float> elements1(array.begin(), array.end());
multiset<float> elements2(sorted.begin(), sorted.end());
ASSERT(elements1 == elements2);
}
void testUniformValues()
{
OpenMM_SFMT::SFMT sfmt;
init_gen_rand(0, sfmt);
vector<float> array(10000);
for (int i = 0; i < (int) array.size(); i++)
array[i] = (float) genrand_real2(sfmt);
verifySorting(array);
}
void testLogValues()
{
OpenMM_SFMT::SFMT sfmt;
init_gen_rand(0, sfmt);
vector<float> array(10000);
for (int i = 0; i < (int) array.size(); i++)
array[i] = (float) log(genrand_real2(sfmt));
verifySorting(array);
}
int main() {
try {
testUniformValues();
testLogValues();
}
catch(const exception& e) {
cout << "exception: " << e.what() << endl;
return 1;
}
cout << "Done" << endl;
return 0;
}
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