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// A C++ wrapper for CUDA
#ifndef _AFJDFJSDFSD_PYCUDA_HEADER_SEEN_CUDA_HPP
#define _AFJDFJSDFSD_PYCUDA_HEADER_SEEN_CUDA_HPP
#include <cuda.h>
#include <stdexcept>
#include <boost/shared_ptr.hpp>
#include <boost/foreach.hpp>
#include <boost/weak_ptr.hpp>
#include <utility>
#include <stack>
#include <iostream>
#include <vector>
#include <boost/python.hpp>
#include <boost/thread/thread.hpp>
#include <boost/thread/tss.hpp>
#include <boost/version.hpp>
#if (BOOST_VERSION/100) < 1035
#warning *****************************************************************
#warning **** Your version of Boost C++ is likely too old for PyCUDA. ****
#warning *****************************************************************
// #define CUDAPP_TRACE_CUDA
#ifdef CUDAPP_TRACE_CUDA
#define CUDAPP_PRINT_CALL_TRACE(NAME) \
std::cerr << NAME << std::endl;
#define CUDAPP_PRINT_CALL_TRACE_INFO(NAME, EXTRA_INFO) \
std::cerr << NAME << " (" << EXTRA_INFO << ')' << std::endl;
#else
#define CUDAPP_PRINT_CALL_TRACE(NAME) /*nothing*/
#define CUDAPP_PRINT_CALL_TRACE_INFO(NAME, EXTRA_INFO) /*nothing*/
#define CUDAPP_CALL_GUARDED_THREADED_WITH_TRACE_INFO(NAME, ARGLIST, TRACE_INFO) \
{ \
CUDAPP_PRINT_CALL_TRACE_INFO(#NAME, TRACE_INFO); \
CUresult cu_status_code; \
Py_BEGIN_ALLOW_THREADS \
cu_status_code = NAME ARGLIST; \
Py_END_ALLOW_THREADS \
if (cu_status_code != CUDA_SUCCESS) \
throw cuda::error(#NAME, cu_status_code);\
}
#define CUDAPP_CALL_GUARDED_WITH_TRACE_INFO(NAME, ARGLIST, TRACE_INFO) \
{ \
CUDAPP_PRINT_CALL_TRACE_INFO(#NAME, TRACE_INFO); \
CUresult cu_status_code; \
cu_status_code = NAME ARGLIST; \
if (cu_status_code != CUDA_SUCCESS) \
throw cuda::error(#NAME, cu_status_code);\
}
#define CUDAPP_CALL_GUARDED_THREADED(NAME, ARGLIST) \
CUDAPP_PRINT_CALL_TRACE(#NAME); \
CUresult cu_status_code; \
Py_BEGIN_ALLOW_THREADS \
cu_status_code = NAME ARGLIST; \
Py_END_ALLOW_THREADS \
if (cu_status_code != CUDA_SUCCESS) \
throw cuda::error(#NAME, cu_status_code);\
}
#define CUDAPP_CALL_GUARDED(NAME, ARGLIST) \
{ \
CUDAPP_PRINT_CALL_TRACE(#NAME); \
CUresult cu_status_code; \
cu_status_code = NAME ARGLIST; \
if (cu_status_code != CUDA_SUCCESS) \
throw cuda::error(#NAME, cu_status_code);\
}
#define CUDAPP_CALL_GUARDED_CLEANUP(NAME, ARGLIST) \
{ \
CUDAPP_PRINT_CALL_TRACE(#NAME); \
CUresult cu_status_code; \
cu_status_code = NAME ARGLIST; \
if (cu_status_code != CUDA_SUCCESS) \
std::cerr \
<< "PyCUDA WARNING: a clean-up operation failed (dead context maybe?)" \
<< std::endl \
<< cuda::error::make_message(#NAME, cu_status_code) \
<< std::endl; \
}
catch (cuda::cannot_activate_out_of_thread_context) \
{ }
// In all likelihood, this TYPE's managing thread has exited, and
// therefore its context has already been deleted. No need to harp
// on the fact that we still thought there was cleanup to do.
// std::cerr << "PyCUDA WARNING: leaked out-of-thread " #TYPE " instance" << std::endl; */
namespace cuda
{
namespace py = boost::python;
class error : public std::runtime_error
{
private:
const char *m_routine;
CUresult m_code;
public:
static std::string make_message(const char *rout, CUresult c, const char *msg=0)
{
std::string result = rout;
result += " failed: ";
result += curesult_to_str(c);
if (msg)
{
result += " - ";
result += msg;
}
return result;
}
error(const char *rout, CUresult c, const char *msg=0)
: std::runtime_error(make_message(rout, c, msg)),
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m_routine(rout), m_code(c)
{ }
const char *routine() const
{
return m_routine;
}
CUresult code() const
{
return m_code;
}
static const char *curesult_to_str(CUresult e)
{
switch (e)
{
case CUDA_SUCCESS: return "success";
case CUDA_ERROR_INVALID_VALUE: return "invalid value";
case CUDA_ERROR_OUT_OF_MEMORY: return "out of memory";
case CUDA_ERROR_NOT_INITIALIZED: return "not initialized";
#if CUDA_VERSION >= 2000
case CUDA_ERROR_DEINITIALIZED: return "deinitialized";
#endif
case CUDA_ERROR_NO_DEVICE: return "no device";
case CUDA_ERROR_INVALID_DEVICE: return "invalid device";
case CUDA_ERROR_INVALID_IMAGE: return "invalid image";
case CUDA_ERROR_INVALID_CONTEXT: return "invalid context";
case CUDA_ERROR_CONTEXT_ALREADY_CURRENT: return "context already current";
case CUDA_ERROR_MAP_FAILED: return "map failed";
case CUDA_ERROR_UNMAP_FAILED: return "unmap failed";
case CUDA_ERROR_ARRAY_IS_MAPPED: return "array is mapped";
case CUDA_ERROR_ALREADY_MAPPED: return "already mapped";
case CUDA_ERROR_NO_BINARY_FOR_GPU: return "no binary for gpu";
case CUDA_ERROR_ALREADY_ACQUIRED: return "already acquired";
case CUDA_ERROR_NOT_MAPPED: return "not mapped";
#if CUDA_VERSION >= 3000
case CUDA_ERROR_NOT_MAPPED_AS_ARRAY: return "not mapped as array";
case CUDA_ERROR_NOT_MAPPED_AS_POINTER: return "not mapped as pointer";
#endif
case CUDA_ERROR_INVALID_SOURCE: return "invalid source";
case CUDA_ERROR_FILE_NOT_FOUND: return "file not found";
case CUDA_ERROR_INVALID_HANDLE: return "invalid handle";
case CUDA_ERROR_NOT_FOUND: return "not found";
case CUDA_ERROR_NOT_READY: return "not ready";
case CUDA_ERROR_LAUNCH_FAILED: return "launch failed";
case CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES: return "launch out of resources";
case CUDA_ERROR_LAUNCH_TIMEOUT: return "launch timeout";
case CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING: return "launch incompatible texturing";
#if CUDA_VERSION >= 3000
case CUDA_ERROR_POINTER_IS_64BIT: return "pointer is 64-bit";
case CUDA_ERROR_SIZE_IS_64BIT: return "size is 64-bit";
#endif
case CUDA_ERROR_UNKNOWN: return "unknown";
default: return "invalid error code";
}
}
};
struct cannot_activate_out_of_thread_context : public std::logic_error
{
cannot_activate_out_of_thread_context(std::string const &w)
: std::logic_error(w)
{ }
};
// version query ------------------------------------------------------------
#if CUDA_VERSION >= 2020
inline int get_driver_version()
{
int result;
CUDAPP_CALL_GUARDED(cuDriverGetVersion, (&result));
return result;
}
#endif
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// device -------------------------------------------------------------------
class context;
class device
{
private:
CUdevice m_device;
public:
device(CUdevice dev)
: m_device(dev)
{ }
static int count()
{
int result;
CUDAPP_CALL_GUARDED(cuDeviceGetCount, (&result));
return result;
}
std::string name()
{
char buffer[1024];
CUDAPP_CALL_GUARDED(cuDeviceGetName, (buffer, sizeof(buffer), m_device));
return buffer;
}
py::tuple compute_capability()
{
int major, minor;
CUDAPP_CALL_GUARDED(cuDeviceComputeCapability, (&major, &minor, m_device));
return py::make_tuple(major, minor);
}
unsigned int total_memory()
{
unsigned int bytes;
CUDAPP_CALL_GUARDED(cuDeviceTotalMem, (&bytes, m_device));
return bytes;
}
{
int result;
CUDAPP_CALL_GUARDED(cuDeviceGetAttribute, (&result, attr, m_device));
return result;
}
bool operator==(const device &other) const
{
return m_device == other.m_device;
}
bool operator!=(const device &other) const
{
return m_device != other.m_device;
}
long hash() const
{
return m_device;
}
boost::shared_ptr<context> make_context(unsigned int flags);
CUdevice handle() const
{ return m_device; }
inline
void init(unsigned int flags)
{
CUDAPP_CALL_GUARDED(cuInit, (flags));
}
device *make_device(int ordinal)
{
CUdevice result;
CUDAPP_CALL_GUARDED(cuDeviceGet, (&result, ordinal));
return new device(result);
}
// context ------------------------------------------------------------------
/* A word on context management: We don't let CUDA's context stack get more
* than one deep. CUDA only supports pushing floating contexts. We may wish
* to push contexts that are already active at a deeper stack level, so we
* maintain all contexts floating other than the top one.
*/
// for friend decl
namespace gl {
boost::shared_ptr<context>
make_gl_context(device const &dev, unsigned int flags);
}
typedef std::stack<boost::weak_ptr<context>,
std::vector<boost::weak_ptr<context> > > context_stack_t;
extern boost::thread_specific_ptr<context_stack_t> context_stack_ptr;
inline context_stack_t &get_context_stack()
{
if (context_stack_ptr.get() == 0)
context_stack_ptr.reset(new context_stack_t);
return *context_stack_ptr;
}
class context : boost::noncopyable
{
private:
CUcontext m_context;
bool m_valid;
unsigned m_use_count;
boost::thread::id m_thread;
context(CUcontext ctx)
: m_context(ctx), m_valid(true), m_use_count(1),
m_thread(boost::this_thread::get_id())
{ }
~context()
if (m_use_count)
std::cerr
<< "-----------------------------------------------------------" << std::endl
<< "PyCUDA WARNING: I'm being asked to destroy a " << std::endl
<< "context that's part of the current context stack." << std::endl
<< "-----------------------------------------------------------" << std::endl
<< "I will pick the next lower active context from the" << std::endl
<< "context stack. Since this choice is happening" << std::endl
<< "at an unspecified point in time, your code" << std::endl
<< "may be making false assumptions about which" << std::endl
<< "context is active at what point." << std::endl
<< "Call Context.pop() to avoid this warning." << std::endl
<< "-----------------------------------------------------------" << std::endl
<< "If Python is terminating abnormally (eg. exiting upon an" << std::endl
<< "unhandled exception), you may ignore this." << std::endl
<< "-----------------------------------------------------------" << std::endl;
CUcontext handle() const
{ return m_context; }
boost::thread::id thread_id() const
{ return m_thread; }
void detach()
{
if (m_valid)
{
if (current_context().get() == this)
{
CUDAPP_CALL_GUARDED_CLEANUP(cuCtxDetach, (m_context));
{
if (m_thread == boost::this_thread::get_id())
{
CUDAPP_CALL_GUARDED_CLEANUP(cuCtxDestroy, (m_context));
}
else
{
// In all likelihood, this context's managing thread has exited, and
// therefore this context has already been deleted. No need to harp
// on the fact that we still thought there was cleanup to do.
// std::cerr << "PyCUDA WARNING: leaked out-of-thread context " << std::endl;
}
}
m_valid = false;
boost::shared_ptr<context> new_active = current_context(this);
if (new_active.get())
CUDAPP_CALL_GUARDED(cuCtxPushCurrent, (new_active->m_context));
}
else
throw error("context::detach", CUDA_ERROR_INVALID_CONTEXT,
"cannot detach from invalid context");
}
static device get_device()
{
CUdevice dev;
CUDAPP_CALL_GUARDED(cuCtxGetDevice, (&dev));
return device(dev);
}
#if CUDA_VERSION >= 2000
static void prepare_context_switch()
{
if (get_context_stack().size())
{
CUcontext popped;
CUDAPP_CALL_GUARDED(cuCtxPopCurrent, (&popped));
}
}
void pop()
{
prepare_context_switch();
get_context_stack().pop();
--m_use_count;
boost::shared_ptr<context> current = current_context();
if (current)
CUDAPP_CALL_GUARDED(cuCtxPushCurrent, (current_context()->m_context));
}
#else
static void prepare_context_switch() { }
#endif
static void synchronize()
{ CUDAPP_CALL_GUARDED_THREADED(cuCtxSynchronize, ()); }
static boost::shared_ptr<context> current_context(context *except=0)
while (true)
{
if (get_context_stack().size() == 0)
return boost::shared_ptr<context>();
boost::weak_ptr<context> result(get_context_stack().top());
if (!result.expired() && result.lock().get() != except)
// good, weak pointer didn't expire
// (treating except as expired weak pointer)
return result.lock();
}
else
{
// weak pointer invalidated, pop it and try again.
get_context_stack().pop();
}
friend class device;
friend void context_push(boost::shared_ptr<context> ctx);
friend boost::shared_ptr<context>
gl::make_gl_context(device const &dev, unsigned int flags);
};
boost::shared_ptr<context> device::make_context(unsigned int flags)
{
context::prepare_context_switch();
CUcontext ctx;
CUDAPP_CALL_GUARDED(cuCtxCreate, (&ctx, flags, m_device));
boost::shared_ptr<context> result(new context(ctx));
get_context_stack().push(result);
return result;
}
#if CUDA_VERSION >= 2000
void context_push(boost::shared_ptr<context> ctx)
{
context::prepare_context_switch();
CUDAPP_CALL_GUARDED(cuCtxPushCurrent, (ctx->m_context));
get_context_stack().push(ctx);
++ctx->m_use_count;
}
#endif
class explicit_context_dependent
{
private:
boost::shared_ptr<context> m_ward_context;
public:
void acquire_context()
{
m_ward_context = context::current_context();
if (m_ward_context.get() == 0)
throw error("explicit_context_dependent",
CUDA_ERROR_INVALID_CONTEXT,
"no currently active context?");
}
void release_context()
{
m_ward_context.reset();
}
boost::shared_ptr<context> get_context()
{
return m_ward_context;
}
class context_dependent : public explicit_context_dependent
{
private:
boost::shared_ptr<context> m_ward_context;
public:
context_dependent()
{ acquire_context(); }
};
class scoped_context_activation
{
private:
boost::shared_ptr<context> m_context;
bool m_did_switch;
public:
scoped_context_activation(boost::shared_ptr<context> ctx)
: m_context(ctx)
{
m_did_switch = context::current_context() != m_context;
if (m_did_switch)
{
if (boost::this_thread::get_id() != m_context->thread_id())
throw cuda::cannot_activate_out_of_thread_context(
"cannot activate out-of-thread context");
#if CUDA_VERSION >= 2000
context_push(m_context);
#else
throw cuda::error("scoped_context_activation", CUDA_ERROR_INVALID_CONTEXT,
"not available in CUDA < 2.0");
#endif
}
~scoped_context_activation()
#if CUDA_VERSION >= 2000
if (m_did_switch)
m_context->pop();
#endif
// streams ------------------------------------------------------------------
class stream : public boost::noncopyable, public context_dependent
{
private:
CUstream m_stream;
public:
stream(unsigned int flags=0)
{ CUDAPP_CALL_GUARDED(cuStreamCreate, (&m_stream, flags)); }
~stream()
try
{
scoped_context_activation ca(get_context());
CUDAPP_CALL_GUARDED_CLEANUP(cuStreamDestroy, (m_stream));
}
void synchronize()
{ CUDAPP_CALL_GUARDED_THREADED(cuStreamSynchronize, (m_stream)); }
CUstream handle() const
{ return m_stream; }
bool is_done() const
{
#ifdef TRACE_CUDA
std::cerr << "cuStreamQuery" << std::endl;
#endif
CUresult result = cuStreamQuery(m_stream);
switch (result)
{
case CUDA_SUCCESS:
return true;
case CUDA_ERROR_NOT_READY:
return false;
default:
throw error("cuStreamQuery", result);
}
}
};
// arrays -------------------------------------------------------------------
class array : public boost::noncopyable, public context_dependent
{
private:
CUarray m_array;
bool m_managed;
public:
array(const CUDA_ARRAY_DESCRIPTOR &descr)
{ CUDAPP_CALL_GUARDED(cuArrayCreate, (&m_array, &descr)); }
#if CUDA_VERSION >= 2000
array(const CUDA_ARRAY3D_DESCRIPTOR &descr)
{ CUDAPP_CALL_GUARDED(cuArray3DCreate, (&m_array, &descr)); }
#endif
array(CUarray ary, bool managed)
{ }
~array()
{ free(); }
void free()
{
if (m_managed)
{
try
{
scoped_context_activation ca(get_context());
CUDAPP_CALL_GUARDED_CLEANUP(cuArrayDestroy, (m_array));
m_managed = false;
release_context();
}
}
CUDA_ARRAY_DESCRIPTOR get_descriptor()
{
CUDA_ARRAY_DESCRIPTOR result;
CUDAPP_CALL_GUARDED(cuArrayGetDescriptor, (&result, m_array));
return result;
}
#if CUDA_VERSION >= 2000
CUDA_ARRAY3D_DESCRIPTOR get_descriptor_3d()
{
CUDA_ARRAY3D_DESCRIPTOR result;
CUDAPP_CALL_GUARDED(cuArray3DGetDescriptor, (&result, m_array));
return result;
}
#endif
CUarray handle() const
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{ return m_array; }
};
// texture reference --------------------------------------------------------
class module;
class texture_reference : public boost::noncopyable
{
private:
CUtexref m_texref;
bool m_managed;
// life support for array and module
boost::shared_ptr<array> m_array;
boost::shared_ptr<module> m_module;
public:
texture_reference()
: m_managed(true)
{ CUDAPP_CALL_GUARDED(cuTexRefCreate, (&m_texref)); }
texture_reference(CUtexref tr, bool managed)
: m_texref(tr), m_managed(managed)
{ }
~texture_reference()
{
if (m_managed)
{
CUDAPP_CALL_GUARDED_CLEANUP(cuTexRefDestroy, (m_texref));
}
}
void set_module(boost::shared_ptr<module> mod)
{ m_module = mod; }
CUtexref handle() const
{ return m_texref; }
void set_array(boost::shared_ptr<array> ary)
{
CUDAPP_CALL_GUARDED(cuTexRefSetArray, (m_texref,
ary->handle(), CU_TRSA_OVERRIDE_FORMAT));
m_array = ary;
}
unsigned int set_address(CUdeviceptr dptr, unsigned int bytes, bool allow_offset=false)
{
unsigned int byte_offset;
CUDAPP_CALL_GUARDED(cuTexRefSetAddress, (&byte_offset,
m_texref, dptr, bytes));
if (!allow_offset && byte_offset != 0)
throw cuda::error("texture_reference::set_address", CUDA_ERROR_INVALID_VALUE,
"texture binding resulted in offset, but allow_offset was false");
m_array.reset();
return byte_offset;
}
void set_address_2d(CUdeviceptr dptr,
const CUDA_ARRAY_DESCRIPTOR &descr, unsigned int pitch)
{
CUDAPP_CALL_GUARDED(cuTexRefSetAddress2D, (m_texref, &descr, dptr, pitch));
}
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void set_format(CUarray_format fmt, int num_packed_components)
{ CUDAPP_CALL_GUARDED(cuTexRefSetFormat, (m_texref, fmt, num_packed_components)); }
void set_address_mode(int dim, CUaddress_mode am)
{ CUDAPP_CALL_GUARDED(cuTexRefSetAddressMode, (m_texref, dim, am)); }
void set_filter_mode(CUfilter_mode fm)
{ CUDAPP_CALL_GUARDED(cuTexRefSetFilterMode, (m_texref, fm)); }
void set_flags(unsigned int flags)
{ CUDAPP_CALL_GUARDED(cuTexRefSetFlags, (m_texref, flags)); }
CUdeviceptr get_address()
{
CUdeviceptr result;
CUDAPP_CALL_GUARDED(cuTexRefGetAddress, (&result, m_texref));
return result;
}
array *get_array()
{
CUarray result;
CUDAPP_CALL_GUARDED(cuTexRefGetArray, (&result, m_texref));
return new array(result, false);
}
CUaddress_mode get_address_mode(int dim)
{
CUaddress_mode result;
CUDAPP_CALL_GUARDED(cuTexRefGetAddressMode, (&result, m_texref, dim));
return result;
}
CUfilter_mode get_filter_mode()
{
CUfilter_mode result;
CUDAPP_CALL_GUARDED(cuTexRefGetFilterMode, (&result, m_texref));
return result;
}
#if CUDA_VERSION >= 2000
py::tuple get_format()
{
CUarray_format fmt;
int num_channels;
CUDAPP_CALL_GUARDED(cuTexRefGetFormat, (&fmt, &num_channels, m_texref));
return py::make_tuple(fmt, num_channels);
}
#endif
unsigned int get_flags()
{
unsigned int result;
CUDAPP_CALL_GUARDED(cuTexRefGetFlags, (&result, m_texref));
return result;
}
};
// module -------------------------------------------------------------------
class function;
class module : public boost::noncopyable, public context_dependent
{
private:
CUmodule m_module;
public:
module(CUmodule mod)
{ }
~module()
{
try
{
scoped_context_activation ca(get_context());
CUDAPP_CALL_GUARDED_CLEANUP(cuModuleUnload, (m_module));
}
CUmodule handle() const
{ return m_module; }
function get_function(const char *name);
py::tuple get_global(const char *name)
{
CUdeviceptr devptr;
unsigned int bytes;
CUDAPP_CALL_GUARDED(cuModuleGetGlobal, (&devptr, &bytes, m_module, name));
return py::make_tuple(devptr, bytes);
}
};
module *module_from_file(const char *filename)
{
CUmodule mod;
CUDAPP_CALL_GUARDED(cuModuleLoad, (&mod, filename));
return new module(mod);
}
texture_reference *module_get_texref(boost::shared_ptr<module> mod, const char *name)
{
CUtexref tr;
CUDAPP_CALL_GUARDED(cuModuleGetTexRef, (&tr, mod->handle(), name));
std::auto_ptr<texture_reference> result(
new texture_reference(tr, false));
result->set_module(mod);
return result.release();
}
// function -----------------------------------------------------------------
class function
{
private:
CUfunction m_function;
function(CUfunction func, std::string const &sym)
: m_function(func), m_symbol(sym)
{ }
void set_block_shape(int x, int y, int z)
{
CUDAPP_CALL_GUARDED_WITH_TRACE_INFO(
cuFuncSetBlockShape, (m_function, x, y, z), m_symbol);
}
void set_shared_size(unsigned int bytes)
{
CUDAPP_CALL_GUARDED_WITH_TRACE_INFO(
cuFuncSetSharedSize, (m_function, bytes), m_symbol);
}
void param_set_size(unsigned int bytes)
{
CUDAPP_CALL_GUARDED_WITH_TRACE_INFO(
cuParamSetSize, (m_function, bytes), m_symbol);
}
void param_set(int offset, unsigned int value)
{
CUDAPP_CALL_GUARDED_WITH_TRACE_INFO(
cuParamSeti, (m_function, offset, value), m_symbol);
}
void param_set(int offset, float value)
{
CUDAPP_CALL_GUARDED_WITH_TRACE_INFO(
cuParamSetf, (m_function, offset, value), m_symbol);
}
void param_setv(int offset, void *buf, unsigned long len)
{
CUDAPP_CALL_GUARDED_WITH_TRACE_INFO(
cuParamSetv, (m_function, offset, buf, len), m_symbol);
}
void param_set_texref(const texture_reference &tr)
{
CUDAPP_CALL_GUARDED_WITH_TRACE_INFO(cuParamSetTexRef, (m_function,
CU_PARAM_TR_DEFAULT, tr.handle()), m_symbol);
}
void launch()
{
CUDAPP_CALL_GUARDED_THREADED_WITH_TRACE_INFO(
cuLaunch, (m_function), m_symbol);
}
void launch_grid(int grid_width, int grid_height)
{
CUDAPP_CALL_GUARDED_THREADED_WITH_TRACE_INFO(
cuLaunchGrid, (m_function, grid_width, grid_height), m_symbol);
}
void launch_grid_async(int grid_width, int grid_height, const stream &s)
{
CUDAPP_CALL_GUARDED_THREADED_WITH_TRACE_INFO(
cuLaunchGridAsync, (m_function, grid_width, grid_height, s.handle()),
m_symbol);
}
#if CUDA_VERSION >= 2020
int get_attribute(CUfunction_attribute attr) const
{
int result;
CUDAPP_CALL_GUARDED_WITH_TRACE_INFO(
cuFuncGetAttribute, (&result, attr, m_function), m_symbol);
function module::get_function(const char *name)
{
CUfunction func;
CUDAPP_CALL_GUARDED(cuModuleGetFunction, (&func, m_module, name));
}
// device memory ------------------------------------------------------------
inline
py::tuple mem_get_info()
{
unsigned int free, total;
CUDAPP_CALL_GUARDED(cuMemGetInfo, (&free, &total));
return py::make_tuple(free, total);
}
inline
CUdeviceptr mem_alloc(unsigned long bytes)
{
CUdeviceptr devptr;
CUDAPP_CALL_GUARDED(cuMemAlloc, (&devptr, bytes));
return devptr;
}
inline
void mem_free(CUdeviceptr devptr)
{
CUDAPP_CALL_GUARDED_CLEANUP(cuMemFree, (devptr));
}
class device_allocation : public boost::noncopyable, public context_dependent
CUdeviceptr m_devptr;