[d1fe925] | 1 | """ |
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| 2 | GPU support through OpenCL |
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| 3 | |
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| 4 | There should be a single GPU environment running on the system. This |
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| 5 | environment is constructed on the first call to :func:`env`, and the |
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| 6 | same environment is returned on each call. |
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| 7 | |
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| 8 | After retrieving the environment, the next step is to create the kernel. |
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| 9 | This is done with a call to :meth:`GpuEnvironment.make_kernel`, which |
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| 10 | returns the type of data used by the kernel. |
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| 11 | |
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| 12 | Next a :class:`GpuData` object should be created with the correct kind |
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| 13 | of data. This data object can be used by multiple kernels, for example, |
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| 14 | if the target model is a weighted sum of multiple kernels. The data |
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| 15 | should include any extra evaluation points required to compute the proper |
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| 16 | data smearing. This need not match the square grid for 2D data if there |
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| 17 | is an index saying which q points are active. |
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| 18 | |
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| 19 | Together the GpuData, the program, and a device form a :class:`GpuKernel`. |
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| 20 | This kernel is used during fitting, receiving new sets of parameters and |
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| 21 | evaluating them. The output value is stored in an output buffer on the |
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| 22 | devices, where it can be combined with other structure factors and form |
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| 23 | factors and have instrumental resolution effects applied. |
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| 24 | """ |
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| 25 | import os |
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| 26 | import warnings |
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| 27 | |
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| 28 | import numpy as np |
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| 29 | |
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| 30 | try: |
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| 31 | import pyopencl as cl |
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| 32 | # Ask OpenCL for the default context so that we know that one exists |
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| 33 | cl.create_some_context(interactive=False) |
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| 34 | except Exception, exc: |
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| 35 | warnings.warn(str(exc)) |
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| 36 | raise RuntimeError("OpenCL not available") |
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| 37 | |
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| 38 | from pyopencl import mem_flags as mf |
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| 39 | |
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| 40 | from . import generate |
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| 41 | |
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| 42 | F64_DEFS = """\ |
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| 43 | #ifdef cl_khr_fp64 |
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| 44 | # pragma OPENCL EXTENSION cl_khr_fp64: enable |
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| 45 | #endif |
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| 46 | """ |
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| 47 | |
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| 48 | # The max loops number is limited by the amount of local memory available |
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| 49 | # on the device. You don't want to make this value too big because it will |
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| 50 | # waste resources, nor too small because it may interfere with users trying |
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| 51 | # to do their polydispersity calculations. A value of 1024 should be much |
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| 52 | # larger than necessary given that cost grows as npts^k where k is the number |
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| 53 | # of polydisperse parameters. |
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| 54 | MAX_LOOPS = 2048 |
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| 55 | |
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| 56 | |
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| 57 | ENV = None |
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| 58 | def environment(): |
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| 59 | """ |
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| 60 | Returns a singleton :class:`GpuEnvironment`. |
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| 61 | |
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| 62 | This provides an OpenCL context and one queue per device. |
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| 63 | """ |
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| 64 | global ENV |
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| 65 | if ENV is None: |
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| 66 | ENV = GpuEnvironment() |
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| 67 | return ENV |
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| 68 | |
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| 69 | def has_double(device): |
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| 70 | """ |
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| 71 | Return true if device supports double precision. |
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| 72 | """ |
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| 73 | return "cl_khr_fp64" in device.extensions |
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| 74 | |
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| 75 | def get_warp(kernel, queue): |
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| 76 | """ |
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| 77 | Return the size of an execution batch for *kernel* running on *queue*. |
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| 78 | """ |
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| 79 | return kernel.get_work_group_info( |
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| 80 | cl.kernel_work_group_info.PREFERRED_WORK_GROUP_SIZE_MULTIPLE, |
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| 81 | queue.device) |
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| 82 | |
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| 83 | def _stretch_input(vector, dtype, extra=1e-3, boundary=32): |
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| 84 | """ |
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| 85 | Stretch an input vector to the correct boundary. |
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| 86 | |
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| 87 | Performance on the kernels can drop by a factor of two or more if the |
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| 88 | number of values to compute does not fall on a nice power of two |
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| 89 | boundary. The trailing additional vector elements are given a |
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| 90 | value of *extra*, and so f(*extra*) will be computed for each of |
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| 91 | them. The returned array will thus be a subset of the computed array. |
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| 92 | |
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| 93 | *boundary* should be a power of 2 which is at least 32 for good |
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| 94 | performance on current platforms (as of Jan 2015). It should |
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| 95 | probably be the max of get_warp(kernel,queue) and |
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| 96 | device.min_data_type_align_size//4. |
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| 97 | """ |
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| 98 | remainder = vector.size % boundary |
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| 99 | if remainder != 0: |
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| 100 | size = vector.size + (boundary - remainder) |
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| 101 | vector = np.hstack((vector, [extra] * (size - vector.size))) |
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| 102 | return np.ascontiguousarray(vector, dtype=dtype) |
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| 103 | |
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| 104 | |
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| 105 | def compile_model(context, source, dtype): |
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| 106 | """ |
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| 107 | Build a model to run on the gpu. |
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| 108 | |
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| 109 | Returns the compiled program and its type. The returned type will |
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| 110 | be float32 even if the desired type is float64 if any of the |
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| 111 | devices in the context do not support the cl_khr_fp64 extension. |
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| 112 | """ |
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| 113 | dtype = np.dtype(dtype) |
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| 114 | if dtype == generate.F64 and not all(has_double(d) for d in context.devices): |
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| 115 | raise RuntimeError("Double precision not supported for devices") |
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| 116 | |
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| 117 | header = F64_DEFS if dtype == generate.F64 else "" |
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| 118 | if dtype == generate.F32: |
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| 119 | source = generate.use_single(source) |
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| 120 | # Note: USE_SINCOS makes the intel cpu slower under opencl |
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| 121 | if context.devices[0].type == cl.device_type.GPU: |
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| 122 | header += "#define USE_SINCOS\n" |
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| 123 | program = cl.Program(context, header + source).build() |
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| 124 | return program |
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| 125 | |
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| 126 | |
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| 127 | def make_result(self, size): |
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| 128 | self.res = np.empty(size, dtype=self.dtype) |
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| 129 | self.res_b = cl.Buffer(self.program.context, mf.READ_WRITE, self.res.nbytes) |
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| 130 | return self.res, self.res_b |
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| 131 | |
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| 132 | |
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| 133 | # for now, this returns one device in the context |
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| 134 | # TODO: create a context that contains all devices on all platforms |
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| 135 | class GpuEnvironment(object): |
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| 136 | """ |
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| 137 | GPU context, with possibly many devices, and one queue per device. |
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| 138 | """ |
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| 139 | def __init__(self): |
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| 140 | # find gpu context |
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| 141 | #self.context = cl.create_some_context() |
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| 142 | |
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| 143 | self.context = None |
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| 144 | if 'PYOPENCL_CTX' in os.environ: |
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| 145 | self._create_some_context() |
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| 146 | |
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| 147 | if not self.context: |
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| 148 | self.context = _get_default_context() |
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| 149 | |
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| 150 | # Byte boundary for data alignment |
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| 151 | #self.data_boundary = max(d.min_data_type_align_size |
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| 152 | # for d in self.context.devices) |
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| 153 | self.queues = [cl.CommandQueue(self.context, d) |
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| 154 | for d in self.context.devices] |
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| 155 | self.has_double = all(has_double(d) for d in self.context.devices) |
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| 156 | self.compiled = {} |
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| 157 | |
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| 158 | def _create_some_context(self): |
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| 159 | try: |
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| 160 | self.context = cl.create_some_context(interactive=False) |
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| 161 | except Exception, exc: |
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| 162 | warnings.warn(str(exc)) |
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| 163 | warnings.warn("pyopencl.create_some_context() failed") |
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| 164 | warnings.warn("the environment variable 'PYOPENCL_CTX' might not be set correctly") |
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| 165 | |
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| 166 | def compile_program(self, name, source, dtype): |
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| 167 | if name not in self.compiled: |
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| 168 | #print "compiling",name |
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| 169 | self.compiled[name] = compile_model(self.context, source, dtype) |
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| 170 | return self.compiled[name] |
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| 171 | |
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| 172 | def release_program(self, name): |
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| 173 | if name in self.compiled: |
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| 174 | self.compiled[name].release() |
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| 175 | del self.compiled[name] |
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| 176 | |
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| 177 | def _get_default_context(): |
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| 178 | default = None |
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| 179 | for platform in cl.get_platforms(): |
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| 180 | for device in platform.get_devices(): |
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| 181 | if device.type == cl.device_type.GPU: |
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| 182 | return cl.Context([device]) |
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| 183 | if default is None: |
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| 184 | default = device |
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| 185 | |
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| 186 | if not default: |
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| 187 | raise RuntimeError("OpenCL device not found") |
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| 188 | |
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| 189 | return cl.Context([default]) |
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| 190 | |
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| 191 | |
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| 192 | class GpuModel(object): |
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| 193 | """ |
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| 194 | GPU wrapper for a single model. |
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| 195 | |
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| 196 | *source* and *info* are the model source and interface as returned |
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| 197 | from :func:`gen.make`. |
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| 198 | |
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| 199 | *dtype* is the desired model precision. Any numpy dtype for single |
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| 200 | or double precision floats will do, such as 'f', 'float32' or 'single' |
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| 201 | for single and 'd', 'float64' or 'double' for double. Double precision |
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| 202 | is an optional extension which may not be available on all devices. |
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| 203 | """ |
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| 204 | def __init__(self, source, info, dtype=generate.F32): |
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| 205 | self.info = info |
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| 206 | self.source = source |
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| 207 | self.dtype = np.dtype(dtype) |
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| 208 | self.program = None # delay program creation |
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| 209 | |
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| 210 | def __getstate__(self): |
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| 211 | state = self.__dict__.copy() |
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| 212 | state['program'] = None |
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| 213 | return state |
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| 214 | |
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| 215 | def __setstate__(self, state): |
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| 216 | self.__dict__ = state.copy() |
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| 217 | |
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| 218 | def __call__(self, q_input): |
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| 219 | if self.dtype != q_input.dtype: |
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| 220 | raise TypeError("data is %s kernel is %s" % (q_input.dtype, self.dtype)) |
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| 221 | if self.program is None: |
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| 222 | compiler = environment().compile_program |
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| 223 | self.program = compiler(self.info['name'], self.source, self.dtype) |
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| 224 | kernel_name = generate.kernel_name(self.info, q_input.is_2D) |
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| 225 | kernel = getattr(self.program, kernel_name) |
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| 226 | return GpuKernel(kernel, self.info, q_input) |
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| 227 | |
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| 228 | def release(self): |
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| 229 | if self.program is not None: |
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| 230 | environment().release_program(self.info['name']) |
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| 231 | self.program = None |
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| 232 | |
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| 233 | def make_input(self, q_vectors): |
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| 234 | """ |
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| 235 | Make q input vectors available to the model. |
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| 236 | |
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| 237 | Note that each model needs its own q vector even if the case of |
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| 238 | mixture models because some models may be OpenCL, some may be |
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| 239 | ctypes and some may be pure python. |
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| 240 | """ |
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| 241 | return GpuInput(q_vectors, dtype=self.dtype) |
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| 242 | |
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| 243 | # TODO: check that we don't need a destructor for buffers which go out of scope |
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| 244 | class GpuInput(object): |
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| 245 | """ |
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| 246 | Make q data available to the gpu. |
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| 247 | |
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| 248 | *q_vectors* is a list of q vectors, which will be *[q]* for 1-D data, |
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| 249 | and *[qx, qy]* for 2-D data. Internally, the vectors will be reallocated |
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| 250 | to get the best performance on OpenCL, which may involve shifting and |
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| 251 | stretching the array to better match the memory architecture. Additional |
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| 252 | points will be evaluated with *q=1e-3*. |
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| 253 | |
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| 254 | *dtype* is the data type for the q vectors. The data type should be |
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| 255 | set to match that of the kernel, which is an attribute of |
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| 256 | :class:`GpuProgram`. Note that not all kernels support double |
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| 257 | precision, so even if the program was created for double precision, |
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| 258 | the *GpuProgram.dtype* may be single precision. |
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| 259 | |
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| 260 | Call :meth:`release` when complete. Even if not called directly, the |
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| 261 | buffer will be released when the data object is freed. |
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| 262 | """ |
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| 263 | def __init__(self, q_vectors, dtype=generate.F32): |
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| 264 | env = environment() |
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| 265 | self.nq = q_vectors[0].size |
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| 266 | self.dtype = np.dtype(dtype) |
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| 267 | self.is_2D = (len(q_vectors) == 2) |
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| 268 | # TODO: stretch input based on get_warp() |
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| 269 | # not doing it now since warp depends on kernel, which is not known |
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| 270 | # at this point, so instead using 32, which is good on the set of |
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| 271 | # architectures tested so far. |
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| 272 | self.q_vectors = [_stretch_input(q, self.dtype, 32) for q in q_vectors] |
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| 273 | self.q_buffers = [ |
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| 274 | cl.Buffer(env.context, mf.READ_ONLY | mf.COPY_HOST_PTR, hostbuf=q) |
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| 275 | for q in self.q_vectors |
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| 276 | ] |
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| 277 | self.global_size = [self.q_vectors[0].size] |
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| 278 | |
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| 279 | def release(self): |
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| 280 | for b in self.q_buffers: |
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| 281 | b.release() |
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| 282 | self.q_buffers = [] |
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| 283 | |
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| 284 | class GpuKernel(object): |
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| 285 | """ |
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| 286 | Callable SAS kernel. |
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| 287 | |
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| 288 | *kernel* is the GpuKernel object to call. |
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| 289 | |
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| 290 | *info* is the module information |
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| 291 | |
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| 292 | *q_input* is the DllInput q vectors at which the kernel should be |
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| 293 | evaluated. |
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| 294 | |
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| 295 | The resulting call method takes the *pars*, a list of values for |
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| 296 | the fixed parameters to the kernel, and *pd_pars*, a list of (value,weight) |
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| 297 | vectors for the polydisperse parameters. *cutoff* determines the |
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| 298 | integration limits: any points with combined weight less than *cutoff* |
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| 299 | will not be calculated. |
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| 300 | |
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| 301 | Call :meth:`release` when done with the kernel instance. |
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| 302 | """ |
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| 303 | def __init__(self, kernel, info, q_input): |
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| 304 | self.q_input = q_input |
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| 305 | self.kernel = kernel |
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| 306 | self.info = info |
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| 307 | self.res = np.empty(q_input.nq, q_input.dtype) |
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| 308 | dim = '2d' if q_input.is_2D else '1d' |
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| 309 | self.fixed_pars = info['partype']['fixed-' + dim] |
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| 310 | self.pd_pars = info['partype']['pd-' + dim] |
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| 311 | |
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| 312 | # Inputs and outputs for each kernel call |
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| 313 | # Note: res may be shorter than res_b if global_size != nq |
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| 314 | env = environment() |
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| 315 | self.loops_b = [cl.Buffer(env.context, mf.READ_WRITE, |
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| 316 | 2 * MAX_LOOPS * q_input.dtype.itemsize) |
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| 317 | for _ in env.queues] |
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| 318 | self.res_b = [cl.Buffer(env.context, mf.READ_WRITE, |
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| 319 | q_input.global_size[0] * q_input.dtype.itemsize) |
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| 320 | for _ in env.queues] |
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| 321 | |
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| 322 | |
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| 323 | def __call__(self, fixed_pars, pd_pars, cutoff=1e-5): |
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| 324 | real = np.float32 if self.q_input.dtype == generate.F32 else np.float64 |
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| 325 | |
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| 326 | device_num = 0 |
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| 327 | queuei = environment().queues[device_num] |
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| 328 | res_bi = self.res_b[device_num] |
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| 329 | nq = np.uint32(self.q_input.nq) |
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| 330 | if pd_pars: |
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| 331 | cutoff = real(cutoff) |
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| 332 | loops_N = [np.uint32(len(p[0])) for p in pd_pars] |
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| 333 | loops = np.hstack(pd_pars) \ |
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| 334 | if pd_pars else np.empty(0, dtype=self.q_input.dtype) |
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| 335 | loops = np.ascontiguousarray(loops.T, self.q_input.dtype).flatten() |
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| 336 | #print "loops",Nloops, loops |
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| 337 | |
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| 338 | #import sys; print >>sys.stderr,"opencl eval",pars |
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| 339 | #print "opencl eval",pars |
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| 340 | if len(loops) > 2 * MAX_LOOPS: |
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| 341 | raise ValueError("too many polydispersity points") |
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| 342 | |
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| 343 | loops_bi = self.loops_b[device_num] |
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| 344 | cl.enqueue_copy(queuei, loops_bi, loops) |
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| 345 | loops_l = cl.LocalMemory(len(loops.data)) |
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| 346 | #ctx = environment().context |
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| 347 | #loops_bi = cl.Buffer(ctx, mf.READ_ONLY|mf.COPY_HOST_PTR, hostbuf=loops) |
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| 348 | dispersed = [loops_bi, loops_l, cutoff] + loops_N |
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| 349 | else: |
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| 350 | dispersed = [] |
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| 351 | fixed = [real(p) for p in fixed_pars] |
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| 352 | args = self.q_input.q_buffers + [res_bi, nq] + dispersed + fixed |
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| 353 | self.kernel(queuei, self.q_input.global_size, None, *args) |
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| 354 | cl.enqueue_copy(queuei, self.res, res_bi) |
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| 355 | |
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| 356 | return self.res |
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| 357 | |
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| 358 | def release(self): |
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| 359 | for b in self.loops_b: |
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| 360 | b.release() |
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| 361 | self.loops_b = [] |
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| 362 | for b in self.res_b: |
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| 363 | b.release() |
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| 364 | self.res_b = [] |
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| 365 | |
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| 366 | def __del__(self): |
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| 367 | self.release() |
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