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| <div class="section" id="cuda-integration"> |
| <h1>CUDA Integration<a class="headerlink" href="#cuda-integration" title="Permalink to this headline">¶</a></h1> |
| <p>Arrow is not limited to CPU buffers (located in the computer’s main memory, |
| also named “host memory”). It also has provisions for accessing buffers |
| located on a CUDA-capable GPU device (in “device memory”).</p> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>This functionality is optional and must have been enabled at build time. |
| If this is not done by your package manager, you might have to build Arrow |
| yourself.</p> |
| </div> |
| <div class="section" id="cuda-contexts"> |
| <h2>CUDA Contexts<a class="headerlink" href="#cuda-contexts" title="Permalink to this headline">¶</a></h2> |
| <p>A CUDA context represents access to a particular CUDA-capable device. |
| For example, this is creating a CUDA context accessing CUDA device number 0:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">pyarrow</span> <span class="k">import</span> <span class="n">cuda</span> |
| <span class="gp">>>> </span><span class="n">ctx</span> <span class="o">=</span> <span class="n">cuda</span><span class="o">.</span><span class="n">Context</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> |
| <span class="go">>>></span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="cuda-buffers"> |
| <h2>CUDA Buffers<a class="headerlink" href="#cuda-buffers" title="Permalink to this headline">¶</a></h2> |
| <p>A CUDA buffer can be created by copying data from host memory to the memory |
| of a CUDA device, using the <a class="reference internal" href="generated/pyarrow.cuda.Context.html#pyarrow.cuda.Context.buffer_from_data" title="pyarrow.cuda.Context.buffer_from_data"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Context.buffer_from_data()</span></code></a> method. |
| The source data can be any Python buffer-like object, including Arrow buffers:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> |
| <span class="gp">>>> </span><span class="n">arr</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">arr</span><span class="o">.</span><span class="n">nbytes</span> |
| <span class="go">16</span> |
| <span class="gp">>>> </span><span class="n">cuda_buf</span> <span class="o">=</span> <span class="n">ctx</span><span class="o">.</span><span class="n">buffer_from_data</span><span class="p">(</span><span class="n">arr</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="nb">type</span><span class="p">(</span><span class="n">cuda_buf</span><span class="p">)</span> |
| <span class="go">pyarrow._cuda.CudaBuffer</span> |
| <span class="gp">>>> </span><span class="n">cuda_buf</span><span class="o">.</span><span class="n">size</span> <span class="c1"># The buffer's size in bytes</span> |
| <span class="go">16</span> |
| <span class="gp">>>> </span><span class="n">cuda_buf</span><span class="o">.</span><span class="n">address</span> <span class="c1"># The buffer's address in device memory</span> |
| <span class="go">30088364544</span> |
| <span class="gp">>>> </span><span class="n">cuda_buf</span><span class="o">.</span><span class="n">context</span><span class="o">.</span><span class="n">device_number</span> |
| <span class="go">0</span> |
| </pre></div> |
| </div> |
| <p>Conversely, you can copy back a CUDA buffer to device memory, getting a regular |
| CPU buffer:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">buf</span> <span class="o">=</span> <span class="n">cuda_buf</span><span class="o">.</span><span class="n">copy_to_host</span><span class="p">()</span> |
| <span class="gp">>>> </span><span class="nb">type</span><span class="p">(</span><span class="n">buf</span><span class="p">)</span> |
| <span class="go">pyarrow.lib.Buffer</span> |
| <span class="gp">>>> </span><span class="n">np</span><span class="o">.</span><span class="n">frombuffer</span><span class="p">(</span><span class="n">buf</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span> |
| <span class="go">array([0, 1, 2, 3], dtype=int32)</span> |
| </pre></div> |
| </div> |
| <div class="admonition warning"> |
| <p class="admonition-title">Warning</p> |
| <p>Many Arrow functions expect a CPU buffer but will not check the buffer’s |
| actual type. You will get a crash if you pass a CUDA buffer to such a |
| function:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">pa</span><span class="o">.</span><span class="n">py_buffer</span><span class="p">(</span><span class="sa">b</span><span class="s2">"x"</span> <span class="o">*</span> <span class="mi">16</span><span class="p">)</span><span class="o">.</span><span class="n">equals</span><span class="p">(</span><span class="n">cuda_buf</span><span class="p">)</span> |
| <span class="go">Segmentation fault</span> |
| </pre></div> |
| </div> |
| </div> |
| </div> |
| <div class="section" id="numba-integration"> |
| <h2>Numba Integration<a class="headerlink" href="#numba-integration" title="Permalink to this headline">¶</a></h2> |
| <p>There is not much you can do directly with Arrow CUDA buffers from Python, |
| but they support interoperation with <a class="reference external" href="https://numba.pydata.org/">Numba</a>, |
| a JIT compiler which can turn Python code into optimized CUDA kernels.</p> |
| <div class="section" id="arrow-to-numba"> |
| <h3>Arrow to Numba<a class="headerlink" href="#arrow-to-numba" title="Permalink to this headline">¶</a></h3> |
| <p>First let’s define a Numba CUDA kernel operating on an <code class="docutils literal notranslate"><span class="pre">int32</span></code> array. Here, |
| we will simply increment each array element (assuming the array is writable):</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numba.cuda</span> |
| |
| <span class="nd">@numba</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">jit</span> |
| <span class="k">def</span> <span class="nf">increment_by_one</span><span class="p">(</span><span class="n">an_array</span><span class="p">):</span> |
| <span class="n">pos</span> <span class="o">=</span> <span class="n">numba</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">pos</span> <span class="o"><</span> <span class="n">an_array</span><span class="o">.</span><span class="n">size</span><span class="p">:</span> |
| <span class="n">an_array</span><span class="p">[</span><span class="n">pos</span><span class="p">]</span> <span class="o">+=</span> <span class="mi">1</span> |
| </pre></div> |
| </div> |
| <p>Then we need to wrap our CUDA buffer into a Numba “device array” with the right |
| array metadata (shape, strides and datatype). This is necessary so that Numba |
| can identify the array’s characteristics and compile the kernel with the |
| appropriate type declarations.</p> |
| <p>In this case the metadata can simply be got from the original Numpy array. |
| Note the GPU data isn’t copied, just pointed to:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">numba.cuda.cudadrv.devicearray</span> <span class="k">import</span> <span class="n">DeviceNDArray</span> |
| <span class="gp">>>> </span><span class="n">device_arr</span> <span class="o">=</span> <span class="n">DeviceNDArray</span><span class="p">(</span><span class="n">arr</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">arr</span><span class="o">.</span><span class="n">strides</span><span class="p">,</span> <span class="n">arr</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">gpu_data</span><span class="o">=</span><span class="n">cuda_buf</span><span class="o">.</span><span class="n">to_numba</span><span class="p">())</span> |
| </pre></div> |
| </div> |
| <p>(ideally we could have defined an Arrow array in CPU memory, copied it to CUDA |
| memory without losing type information, and then invoked the Numba kernel on it |
| without constructing the DeviceNDArray by hand; this is not yet possible)</p> |
| <p>Finally we can run the Numba CUDA kernel on the Numba device array (here |
| with a 16x16 grid size):</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">increment_by_one</span><span class="p">[</span><span class="mi">16</span><span class="p">,</span> <span class="mi">16</span><span class="p">](</span><span class="n">device_arr</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>And the results can be checked by copying back the CUDA buffer to CPU memory:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">np</span><span class="o">.</span><span class="n">frombuffer</span><span class="p">(</span><span class="n">cuda_buf</span><span class="o">.</span><span class="n">copy_to_host</span><span class="p">(),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span> |
| <span class="go">array([1, 2, 3, 4], dtype=int32)</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="numba-to-arrow"> |
| <h3>Numba to Arrow<a class="headerlink" href="#numba-to-arrow" title="Permalink to this headline">¶</a></h3> |
| <p>Conversely, a Numba-created device array can be viewed as an Arrow CUDA buffer, |
| using the <a class="reference internal" href="generated/pyarrow.cuda.CudaBuffer.html#pyarrow.cuda.CudaBuffer.from_numba" title="pyarrow.cuda.CudaBuffer.from_numba"><code class="xref py py-meth docutils literal notranslate"><span class="pre">CudaBuffer.from_numba()</span></code></a> factory method.</p> |
| <p>For the sake of example, let’s first create a Numba device array:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">arr</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">14</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">arr</span> |
| <span class="go">array([10, 11, 12, 13], dtype=int32)</span> |
| <span class="gp">>>> </span><span class="n">device_arr</span> <span class="o">=</span> <span class="n">numba</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">to_device</span><span class="p">(</span><span class="n">arr</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>Then we can create a CUDA buffer pointing the device array’s memory. |
| We don’t need to pass a CUDA context explicitly this time: the appropriate |
| CUDA context is automatically retrieved and adapted from the Numba object.</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">cuda_buf</span> <span class="o">=</span> <span class="n">cuda</span><span class="o">.</span><span class="n">CudaBuffer</span><span class="o">.</span><span class="n">from_numba</span><span class="p">(</span><span class="n">device_arr</span><span class="o">.</span><span class="n">gpu_data</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">cuda_buf</span><span class="o">.</span><span class="n">size</span> |
| <span class="go">16</span> |
| <span class="gp">>>> </span><span class="n">cuda_buf</span><span class="o">.</span><span class="n">address</span> |
| <span class="go">30088364032</span> |
| <span class="gp">>>> </span><span class="n">cuda_buf</span><span class="o">.</span><span class="n">context</span><span class="o">.</span><span class="n">device_number</span> |
| <span class="go">0</span> |
| </pre></div> |
| </div> |
| <p>Of course, we can copy the CUDA buffer back to host memory:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">np</span><span class="o">.</span><span class="n">frombuffer</span><span class="p">(</span><span class="n">cuda_buf</span><span class="o">.</span><span class="n">copy_to_host</span><span class="p">(),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span> |
| <span class="go">array([10, 11, 12, 13], dtype=int32)</span> |
| </pre></div> |
| </div> |
| <div class="admonition seealso"> |
| <p class="admonition-title">See also</p> |
| <p>Documentation for Numba’s <a class="reference external" href="https://numba.pydata.org/numba-doc/latest/cuda/index.html">CUDA support</a>.</p> |
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