diff --git a/docs/_sources/overview.md.txt b/docs/_sources/overview.md.txt index 74bba12f..d7b068b3 100644 --- a/docs/_sources/overview.md.txt +++ b/docs/_sources/overview.md.txt @@ -54,20 +54,29 @@ import numpy as np ``` Error checking is a fundamental best practice in code development and a code -example is provided. For brevity, error checking within the example is omitted. +example is provided. In a future release, this may automatically raise exceptions using a Python object model. ```{code-cell} python -def ASSERT_DRV(err): - if isinstance(err, cuda.CUresult): - if err != cuda.CUresult.CUDA_SUCCESS: - raise RuntimeError("Cuda Error: {}".format(err)) - elif isinstance(err, nvrtc.nvrtcResult): - if err != nvrtc.nvrtcResult.NVRTC_SUCCESS: - raise RuntimeError("Nvrtc Error: {}".format(err)) +def _cudaGetErrorEnum(error): + if isinstance(error, cuda.CUresult): + err, name = cuda.cuGetErrorName(error) + return name if err == cuda.CUresult.CUDA_SUCCESS else "" + elif isinstance(error, nvrtc.nvrtcResult): + return nvrtc.nvrtcGetErrorString(error)[1] else: - raise RuntimeError("Unknown error type: {}".format(err)) + raise RuntimeError('Unknown error type: {}'.format(error)) + +def checkCudaErrors(result): + if result[0].value: + raise RuntimeError("CUDA error code={}({})".format(result[0].value, _cudaGetErrorEnum(result[0]))) + if len(result) == 1: + return None + elif len(result) == 2: + return result[1] + else: + return result[1:] ``` It’s common practice to write CUDA kernels near the top of a translation unit, @@ -95,40 +104,43 @@ Go ahead and compile the kernel into PTX. Remember that this is executed at runt - Compile the program. - Extract PTX from the compiled program. -In the following code example, compilation is targeting compute capability 75, -or Turing architecture, with FMAD enabled. If compilation fails, use -`nvrtcGetProgramLog` to retrieve a compile log for additional information. +In the following code example, the Driver API is initialized so that the NVIDIA driver +and GPU are accessible. Next, the GPU is queried for their compute capability. Finally, +the program is compiled to target our local compute capability architecture with FMAD enabled. ```{code-cell} python +# Initialize CUDA Driver API +checkCudaErrors(cuda.cuInit(0)) + +# Retrieve handle for device 0 +cuDevice = checkCudaErrors(cuda.cuDeviceGet(0)) + +# Derive target architecture for device 0 +major = checkCudaErrors(cuda.cuDeviceGetAttribute(cuda.CUdevice_attribute.CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR, cuDevice)) +minor = checkCudaErrors(cuda.cuDeviceGetAttribute(cuda.CUdevice_attribute.CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR, cuDevice)) +arch_arg = bytes(f'--gpu-architecture=compute_{major}{minor}', 'ascii') + # Create program -err, prog = nvrtc.nvrtcCreateProgram(str.encode(saxpy), b"saxpy.cu", 0, [], []) +prog = checkCudaErrors(nvrtc.nvrtcCreateProgram(str.encode(saxpy), b"saxpy.cu", 0, [], [])) # Compile program -opts = [b"--fmad=false", b"--gpu-architecture=compute_75"] -err, = nvrtc.nvrtcCompileProgram(prog, 2, opts) +opts = [b"--fmad=false", arch_arg] +checkCudaErrors(nvrtc.nvrtcCompileProgram(prog, 2, opts)) # Get PTX from compilation -err, ptxSize = nvrtc.nvrtcGetPTXSize(prog) +ptxSize = checkCudaErrors(nvrtc.nvrtcGetPTXSize(prog)) ptx = b" " * ptxSize -err, = nvrtc.nvrtcGetPTX(prog, ptx) +checkCudaErrors(nvrtc.nvrtcGetPTX(prog, ptx)) ``` Before you can use the PTX or do any work on the GPU, you must create a CUDA context. CUDA contexts are analogous to host processes for the device. In the -following code example, the Driver API is initialized so that the NVIDIA driver -and GPU are accessible. Next, a handle for compute device 0 is passed to -`cuCtxCreate` to designate that GPU for context creation. With the context -created, you can proceed in compiling the CUDA kernel using NVRTC. +following code example, a handle for compute device 0 is passed to +`cuCtxCreate` to designate that GPU for context creation. ```{code-cell} python -# Initialize CUDA Driver API -err, = cuda.cuInit(0) - -# Retrieve handle for device 0 -err, cuDevice = cuda.cuDeviceGet(0) - # Create context -err, context = cuda.cuCtxCreate(0, cuDevice) +context = checkCudaErrors(cuda.cuCtxCreate(0, cuDevice)) ``` With a CUDA context created on device 0, load the PTX generated earlier into a @@ -140,10 +152,8 @@ After loading into the module, extract a specific kernel with # Load PTX as module data and retrieve function ptx = np.char.array(ptx) # Note: Incompatible --gpu-architecture would be detected here -err, module = cuda.cuModuleLoadData(ptx.ctypes.data) -ASSERT_DRV(err) -err, kernel = cuda.cuModuleGetFunction(module, b"saxpy") -ASSERT_DRV(err) +module = checkCudaErrors(cuda.cuModuleLoadData(ptx.ctypes.data)) +kernel = checkCudaErrors(cuda.cuModuleGetFunction(module, b"saxpy")) ``` Next, get all your data prepared and transferred to the GPU. For increased @@ -175,18 +185,18 @@ Python doesn’t have a natural concept of pointers, yet `cuMemcpyHtoDAsync` exp XX. ```{code-cell} python -err, dXclass = cuda.cuMemAlloc(bufferSize) -err, dYclass = cuda.cuMemAlloc(bufferSize) -err, dOutclass = cuda.cuMemAlloc(bufferSize) +dXclass = checkCudaErrors(cuda.cuMemAlloc(bufferSize)) +dYclass = checkCudaErrors(cuda.cuMemAlloc(bufferSize)) +dOutclass = checkCudaErrors(cuda.cuMemAlloc(bufferSize)) -err, stream = cuda.cuStreamCreate(0) +stream = checkCudaErrors(cuda.cuStreamCreate(0)) -err, = cuda.cuMemcpyHtoDAsync( +checkCudaErrors(cuda.cuMemcpyHtoDAsync( dXclass, hX.ctypes.data, bufferSize, stream -) -err, = cuda.cuMemcpyHtoDAsync( +)) +checkCudaErrors(cuda.cuMemcpyHtoDAsync( dYclass, hY.ctypes.data, bufferSize, stream -) +)) ``` With data prep and resources allocation finished, the kernel is ready to be @@ -213,7 +223,7 @@ args = np.array([arg.ctypes.data for arg in args], dtype=np.uint64) Now the kernel can be launched: ```{code-cell} python -err, = cuda.cuLaunchKernel( +checkCudaErrors(cuda.cuLaunchKernel( kernel, NUM_BLOCKS, # grid x dim 1, # grid y dim @@ -225,12 +235,12 @@ err, = cuda.cuLaunchKernel( stream, # stream args.ctypes.data, # kernel arguments 0, # extra (ignore) -) +)) -err, = cuda.cuMemcpyDtoHAsync( +checkCudaErrors(cuda.cuMemcpyDtoHAsync( hOut.ctypes.data, dOutclass, bufferSize, stream -) -err, = cuda.cuStreamSynchronize(stream) +)) +checkCudaErrors(cuda.cuStreamSynchronize(stream)) ``` The `cuLaunchKernel` function takes the compiled module kernel and execution @@ -252,12 +262,12 @@ Perform verification of the data to ensure correctness and finish the code with memory clean up. ```{code-cell} python -err, = cuda.cuStreamDestroy(stream) -err, = cuda.cuMemFree(dXclass) -err, = cuda.cuMemFree(dYclass) -err, = cuda.cuMemFree(dOutclass) -err, = cuda.cuModuleUnload(module) -err, = cuda.cuCtxDestroy(context) +checkCudaErrors(cuda.cuStreamDestroy(stream)) +checkCudaErrors(cuda.cuMemFree(dXclass)) +checkCudaErrors(cuda.cuMemFree(dYclass)) +checkCudaErrors(cuda.cuMemFree(dOutclass)) +checkCudaErrors(cuda.cuModuleUnload(module)) +checkCudaErrors(cuda.cuCtxDestroy(context)) ``` ## Performance diff --git a/docs/overview.html b/docs/overview.html index f6058234..207be4d2 100644 --- a/docs/overview.html +++ b/docs/overview.html @@ -272,20 +272,29 @@

CUDA Python workflow

Error checking is a fundamental best practice in code development and a code -example is provided. For brevity, error checking within the example is omitted. +example is provided. In a future release, this may automatically raise exceptions using a Python object model.

-
def ASSERT_DRV(err):
-    if isinstance(err, cuda.CUresult):
-        if err != cuda.CUresult.CUDA_SUCCESS:
-            raise RuntimeError("Cuda Error: {}".format(err))
-    elif isinstance(err, nvrtc.nvrtcResult):
-        if err != nvrtc.nvrtcResult.NVRTC_SUCCESS:
-            raise RuntimeError("Nvrtc Error: {}".format(err))
+
def _cudaGetErrorEnum(error):
+    if isinstance(error, cuda.CUresult):
+        err, name = cuda.cuGetErrorName(error)
+        return name if err == cuda.CUresult.CUDA_SUCCESS else "<unknown>"
+    elif isinstance(error, nvrtc.nvrtcResult):
+        return nvrtc.nvrtcGetErrorString(error)[1]
     else:
-        raise RuntimeError("Unknown error type: {}".format(err))
+        raise RuntimeError('Unknown error type: {}'.format(error))
+
+def checkCudaErrors(result):
+    if result[0].value:
+        raise RuntimeError("CUDA error code={}({})".format(result[0].value, _cudaGetErrorEnum(result[0])))
+    if len(result) == 1:
+        return None
+    elif len(result) == 2:
+        return result[1]
+    else:
+        return result[1:]
@@ -318,42 +327,65 @@

CUDA Python workflow

Compile the program.

  • Extract PTX from the compiled program.

    • -

    In the following code example, compilation is targeting compute capability 75, -or Turing architecture, with FMAD enabled. If compilation fails, use -nvrtcGetProgramLog to retrieve a compile log for additional information.

    +

    In the following code example, the Driver API is initialized so that the NVIDIA driver +and GPU are accessible. Next, the GPU is queried for their compute capability. Finally, +the program is compiled to target our local compute capability architecture with FMAD enabled.

    -
    # Create program
-err, prog = nvrtc.nvrtcCreateProgram(str.encode(saxpy), b"saxpy.cu", 0, [], [])
+
    # Initialize CUDA Driver API
+checkCudaErrors(cuda.cuInit(0))
+
+# Retrieve handle for device 0
+cuDevice = checkCudaErrors(cuda.cuDeviceGet(0))
+
+# Derive target architecture for device 0
+major = checkCudaErrors(cuda.cuDeviceGetAttribute(cuda.CUdevice_attribute.CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR, cuDevice))
+minor = checkCudaErrors(cuda.cuDeviceGetAttribute(cuda.CUdevice_attribute.CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR, cuDevice))
+arch_arg = bytes(f'--gpu-architecture=compute_{major}{minor}', 'ascii')
+
+# Create program
+prog = checkCudaErrors(nvrtc.nvrtcCreateProgram(str.encode(saxpy), b"saxpy.cu", 0, [], []))
 
 # Compile program
-opts = [b"--fmad=false", b"--gpu-architecture=compute_75"]
-err, = nvrtc.nvrtcCompileProgram(prog, 2, opts)
+opts = [b"--fmad=false", arch_arg]
+checkCudaErrors(nvrtc.nvrtcCompileProgram(prog, 2, opts))
 
 # Get PTX from compilation
-err, ptxSize = nvrtc.nvrtcGetPTXSize(prog)
+ptxSize = checkCudaErrors(nvrtc.nvrtcGetPTXSize(prog))
 ptx = b" " * ptxSize
-err, = nvrtc.nvrtcGetPTX(prog, ptx)
+checkCudaErrors(nvrtc.nvrtcGetPTX(prog, ptx))
+
    
+
    
+
    +
    +
    ---------------------------------------------------------------------------
+RuntimeError                              Traceback (most recent call last)
+Cell In[4], line 2
+      1 # Initialize CUDA Driver API
+----> 2 checkCudaErrors(cuda.cuInit(0))
+      4 # Retrieve handle for device 0
+      5 cuDevice = checkCudaErrors(cuda.cuDeviceGet(0))
+
+Cell In[2], line 12, in checkCudaErrors(result)
+     10 def checkCudaErrors(result):
+     11     if result[0].value:
+---> 12         raise RuntimeError("CUDA error code={}({})".format(result[0].value, _cudaGetErrorEnum(result[0])))
+     13     if len(result) == 1:
+     14         return None
+
+RuntimeError: CUDA error code=804(b'CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE')

    Before you can use the PTX or do any work on the GPU, you must create a CUDA context. CUDA contexts are analogous to host processes for the device. In the -following code example, the Driver API is initialized so that the NVIDIA driver -and GPU are accessible. Next, a handle for compute device 0 is passed to -cuCtxCreate to designate that GPU for context creation. With the context -created, you can proceed in compiling the CUDA kernel using NVRTC.

    +following code example, a handle for compute device 0 is passed to +cuCtxCreate to designate that GPU for context creation.

    -
    # Initialize CUDA Driver API
-err, = cuda.cuInit(0)
-
-# Retrieve handle for device 0
-err, cuDevice = cuda.cuDeviceGet(0)
-
-# Create context
-err, context = cuda.cuCtxCreate(0, cuDevice)
+
    # Create context
+context = checkCudaErrors(cuda.cuCtxCreate(0, cuDevice))
    @@ -367,31 +399,8 @@

    CUDA Python workflow
    # Load PTX as module data and retrieve function
 ptx = np.char.array(ptx)
 # Note: Incompatible --gpu-architecture would be detected here
-err, module = cuda.cuModuleLoadData(ptx.ctypes.data)
-ASSERT_DRV(err)
-err, kernel = cuda.cuModuleGetFunction(module, b"saxpy")
-ASSERT_DRV(err)
-
    -

    -
    -
    -
    ---------------------------------------------------------------------------
-RuntimeError                              Traceback (most recent call last)
-Cell In[6], line 5
-      3 # Note: Incompatible --gpu-architecture would be detected here
-      4 err, module = cuda.cuModuleLoadData(ptx.ctypes.data)
-----> 5 ASSERT_DRV(err)
-      6 err, kernel = cuda.cuModuleGetFunction(module, b"saxpy")
-      7 ASSERT_DRV(err)
-
-Cell In[2], line 4, in ASSERT_DRV(err)
-      2 if isinstance(err, cuda.CUresult):
-      3     if err != cuda.CUresult.CUDA_SUCCESS:
-----> 4         raise RuntimeError("Cuda Error: {}".format(err))
-      5 elif isinstance(err, nvrtc.nvrtcResult):
-      6     if err != nvrtc.nvrtcResult.NVRTC_SUCCESS:
-
-RuntimeError: Cuda Error: 222
+module = checkCudaErrors(cuda.cuModuleLoadData(ptx.ctypes.data))
+kernel = checkCudaErrors(cuda.cuModuleGetFunction(module, b"saxpy"))
    @@ -426,18 +435,18 @@

    CUDA Python workflow
    -
    err, dXclass = cuda.cuMemAlloc(bufferSize)
-err, dYclass = cuda.cuMemAlloc(bufferSize)
-err, dOutclass = cuda.cuMemAlloc(bufferSize)
+
    dXclass = checkCudaErrors(cuda.cuMemAlloc(bufferSize))
+dYclass = checkCudaErrors(cuda.cuMemAlloc(bufferSize))
+dOutclass = checkCudaErrors(cuda.cuMemAlloc(bufferSize))
 
-err, stream = cuda.cuStreamCreate(0)
+stream = checkCudaErrors(cuda.cuStreamCreate(0))
 
-err, = cuda.cuMemcpyHtoDAsync(
+checkCudaErrors(cuda.cuMemcpyHtoDAsync(
    dXclass, hX.ctypes.data, bufferSize, stream
-)
-err, = cuda.cuMemcpyHtoDAsync(
+))
+checkCudaErrors(cuda.cuMemcpyHtoDAsync(
    dYclass, hY.ctypes.data, bufferSize, stream
-)
+))
    @@ -467,7 +476,7 @@

    CUDA Python workflowNow the kernel can be launched:

    -
    err, = cuda.cuLaunchKernel(
+
    checkCudaErrors(cuda.cuLaunchKernel(
    kernel,
    NUM_BLOCKS,  # grid x dim
    1,  # grid y dim
@@ -479,12 +488,12 @@ 
    CUDA Python workflowstream,  # stream
    args.ctypes.data,  # kernel arguments
    0,  # extra (ignore)
-)
+))
 
-err, = cuda.cuMemcpyDtoHAsync(
+checkCudaErrors(cuda.cuMemcpyDtoHAsync(
    hOut.ctypes.data, dOutclass, bufferSize, stream
-)
-err, = cuda.cuStreamSynchronize(stream)
+))
+checkCudaErrors(cuda.cuStreamSynchronize(stream))
    @@ -510,12 +519,12 @@

    CUDA Python workflow
    -
    err, = cuda.cuStreamDestroy(stream)
-err, = cuda.cuMemFree(dXclass)
-err, = cuda.cuMemFree(dYclass)
-err, = cuda.cuMemFree(dOutclass)
-err, = cuda.cuModuleUnload(module)
-err, = cuda.cuCtxDestroy(context)
+
    checkCudaErrors(cuda.cuStreamDestroy(stream))
+checkCudaErrors(cuda.cuMemFree(dXclass))
+checkCudaErrors(cuda.cuMemFree(dYclass))
+checkCudaErrors(cuda.cuMemFree(dOutclass))
+checkCudaErrors(cuda.cuModuleUnload(module))
+checkCudaErrors(cuda.cuCtxDestroy(context))
    diff --git a/docs/searchindex.js b/docs/searchindex.js index 551adbce..820a6cfe 100644 --- a/docs/searchindex.js +++ b/docs/searchindex.js @@ -1 +1 @@ -Search.setIndex({"docnames": ["api", "conduct", "contribute", "index", "install", "module/cuda", "module/cudart", "module/nvrtc", "motivation", "overview", "release", "release/11.4.0-notes", "release/11.5.0-notes", "release/11.6.0-notes", "release/11.6.1-notes", "release/11.7.0-notes", "release/11.7.1-notes", "release/11.8.0-notes", "release/11.8.1-notes", "release/11.8.2-notes", "release/11.8.3-notes", "release/12.0.0-notes", "release/12.1.0-notes", "release/12.2.0-notes", "release/12.2.1-notes", "release/12.3.0-notes", "release/12.4.0-notes", "release/12.5.0-notes"], "filenames": ["api.rst", "conduct.md", "contribute.md", "index.rst", "install.md", "module/cuda.rst", "module/cudart.rst", "module/nvrtc.rst", "motivation.md", "overview.md", "release.md", "release/11.4.0-notes.md", "release/11.5.0-notes.md", 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14], "default": 13, "primit": 13, "1": [14, 16, 18, 22, 24], "7": [15, 16], "8": [17, 18, 19, 20], "2": [19, 23, 24], "3": [20, 25], "12": [21, 22, 23, 24, 25, 26, 27]}, "envversion": {"sphinx.domains.c": 2, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 6, "sphinx.domains.index": 1, "sphinx.domains.javascript": 2, "sphinx.domains.math": 2, "sphinx.domains.python": 3, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "sphinx": 56}}) \ No newline at end of file diff --git a/docs_src/source/overview.md b/docs_src/source/overview.md index 74bba12f..d7b068b3 100644 --- a/docs_src/source/overview.md +++ b/docs_src/source/overview.md @@ -54,20 +54,29 @@ import numpy as np ``` Error checking is a fundamental best practice in code development and a code -example is provided. For brevity, error checking within the example is omitted. +example is provided. In a future release, this may automatically raise exceptions using a Python object model. ```{code-cell} python -def ASSERT_DRV(err): - if isinstance(err, cuda.CUresult): - if err != cuda.CUresult.CUDA_SUCCESS: - raise RuntimeError("Cuda Error: {}".format(err)) - elif isinstance(err, nvrtc.nvrtcResult): - if err != nvrtc.nvrtcResult.NVRTC_SUCCESS: - raise RuntimeError("Nvrtc Error: {}".format(err)) +def _cudaGetErrorEnum(error): + if isinstance(error, cuda.CUresult): + err, name = cuda.cuGetErrorName(error) + return name if err == cuda.CUresult.CUDA_SUCCESS else "" + elif isinstance(error, nvrtc.nvrtcResult): + return nvrtc.nvrtcGetErrorString(error)[1] else: - raise RuntimeError("Unknown error type: {}".format(err)) + raise RuntimeError('Unknown error type: {}'.format(error)) + +def checkCudaErrors(result): + if result[0].value: + raise RuntimeError("CUDA error code={}({})".format(result[0].value, _cudaGetErrorEnum(result[0]))) + if len(result) == 1: + return None + elif len(result) == 2: + return result[1] + else: + return result[1:] ``` It’s common practice to write CUDA kernels near the top of a translation unit, @@ -95,40 +104,43 @@ Go ahead and compile the kernel into PTX. Remember that this is executed at runt - Compile the program. - Extract PTX from the compiled program. -In the following code example, compilation is targeting compute capability 75, -or Turing architecture, with FMAD enabled. If compilation fails, use -`nvrtcGetProgramLog` to retrieve a compile log for additional information. +In the following code example, the Driver API is initialized so that the NVIDIA driver +and GPU are accessible. Next, the GPU is queried for their compute capability. Finally, +the program is compiled to target our local compute capability architecture with FMAD enabled. ```{code-cell} python +# Initialize CUDA Driver API +checkCudaErrors(cuda.cuInit(0)) + +# Retrieve handle for device 0 +cuDevice = checkCudaErrors(cuda.cuDeviceGet(0)) + +# Derive target architecture for device 0 +major = checkCudaErrors(cuda.cuDeviceGetAttribute(cuda.CUdevice_attribute.CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR, cuDevice)) +minor = checkCudaErrors(cuda.cuDeviceGetAttribute(cuda.CUdevice_attribute.CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR, cuDevice)) +arch_arg = bytes(f'--gpu-architecture=compute_{major}{minor}', 'ascii') + # Create program -err, prog = nvrtc.nvrtcCreateProgram(str.encode(saxpy), b"saxpy.cu", 0, [], []) +prog = checkCudaErrors(nvrtc.nvrtcCreateProgram(str.encode(saxpy), b"saxpy.cu", 0, [], [])) # Compile program -opts = [b"--fmad=false", b"--gpu-architecture=compute_75"] -err, = nvrtc.nvrtcCompileProgram(prog, 2, opts) +opts = [b"--fmad=false", arch_arg] +checkCudaErrors(nvrtc.nvrtcCompileProgram(prog, 2, opts)) # Get PTX from compilation -err, ptxSize = nvrtc.nvrtcGetPTXSize(prog) +ptxSize = checkCudaErrors(nvrtc.nvrtcGetPTXSize(prog)) ptx = b" " * ptxSize -err, = nvrtc.nvrtcGetPTX(prog, ptx) +checkCudaErrors(nvrtc.nvrtcGetPTX(prog, ptx)) ``` Before you can use the PTX or do any work on the GPU, you must create a CUDA context. CUDA contexts are analogous to host processes for the device. In the -following code example, the Driver API is initialized so that the NVIDIA driver -and GPU are accessible. Next, a handle for compute device 0 is passed to -`cuCtxCreate` to designate that GPU for context creation. With the context -created, you can proceed in compiling the CUDA kernel using NVRTC. +following code example, a handle for compute device 0 is passed to +`cuCtxCreate` to designate that GPU for context creation. ```{code-cell} python -# Initialize CUDA Driver API -err, = cuda.cuInit(0) - -# Retrieve handle for device 0 -err, cuDevice = cuda.cuDeviceGet(0) - # Create context -err, context = cuda.cuCtxCreate(0, cuDevice) +context = checkCudaErrors(cuda.cuCtxCreate(0, cuDevice)) ``` With a CUDA context created on device 0, load the PTX generated earlier into a @@ -140,10 +152,8 @@ After loading into the module, extract a specific kernel with # Load PTX as module data and retrieve function ptx = np.char.array(ptx) # Note: Incompatible --gpu-architecture would be detected here -err, module = cuda.cuModuleLoadData(ptx.ctypes.data) -ASSERT_DRV(err) -err, kernel = cuda.cuModuleGetFunction(module, b"saxpy") -ASSERT_DRV(err) +module = checkCudaErrors(cuda.cuModuleLoadData(ptx.ctypes.data)) +kernel = checkCudaErrors(cuda.cuModuleGetFunction(module, b"saxpy")) ``` Next, get all your data prepared and transferred to the GPU. For increased @@ -175,18 +185,18 @@ Python doesn’t have a natural concept of pointers, yet `cuMemcpyHtoDAsync` exp XX. ```{code-cell} python -err, dXclass = cuda.cuMemAlloc(bufferSize) -err, dYclass = cuda.cuMemAlloc(bufferSize) -err, dOutclass = cuda.cuMemAlloc(bufferSize) +dXclass = checkCudaErrors(cuda.cuMemAlloc(bufferSize)) +dYclass = checkCudaErrors(cuda.cuMemAlloc(bufferSize)) +dOutclass = checkCudaErrors(cuda.cuMemAlloc(bufferSize)) -err, stream = cuda.cuStreamCreate(0) +stream = checkCudaErrors(cuda.cuStreamCreate(0)) -err, = cuda.cuMemcpyHtoDAsync( +checkCudaErrors(cuda.cuMemcpyHtoDAsync( dXclass, hX.ctypes.data, bufferSize, stream -) -err, = cuda.cuMemcpyHtoDAsync( +)) +checkCudaErrors(cuda.cuMemcpyHtoDAsync( dYclass, hY.ctypes.data, bufferSize, stream -) +)) ``` With data prep and resources allocation finished, the kernel is ready to be @@ -213,7 +223,7 @@ args = np.array([arg.ctypes.data for arg in args], dtype=np.uint64) Now the kernel can be launched: ```{code-cell} python -err, = cuda.cuLaunchKernel( +checkCudaErrors(cuda.cuLaunchKernel( kernel, NUM_BLOCKS, # grid x dim 1, # grid y dim @@ -225,12 +235,12 @@ err, = cuda.cuLaunchKernel( stream, # stream args.ctypes.data, # kernel arguments 0, # extra (ignore) -) +)) -err, = cuda.cuMemcpyDtoHAsync( +checkCudaErrors(cuda.cuMemcpyDtoHAsync( hOut.ctypes.data, dOutclass, bufferSize, stream -) -err, = cuda.cuStreamSynchronize(stream) +)) +checkCudaErrors(cuda.cuStreamSynchronize(stream)) ``` The `cuLaunchKernel` function takes the compiled module kernel and execution @@ -252,12 +262,12 @@ Perform verification of the data to ensure correctness and finish the code with memory clean up. ```{code-cell} python -err, = cuda.cuStreamDestroy(stream) -err, = cuda.cuMemFree(dXclass) -err, = cuda.cuMemFree(dYclass) -err, = cuda.cuMemFree(dOutclass) -err, = cuda.cuModuleUnload(module) -err, = cuda.cuCtxDestroy(context) +checkCudaErrors(cuda.cuStreamDestroy(stream)) +checkCudaErrors(cuda.cuMemFree(dXclass)) +checkCudaErrors(cuda.cuMemFree(dYclass)) +checkCudaErrors(cuda.cuMemFree(dOutclass)) +checkCudaErrors(cuda.cuModuleUnload(module)) +checkCudaErrors(cuda.cuCtxDestroy(context)) ``` ## Performance