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15 <!--*********************************************************************-->
16 <h1>Polly - GPGPU Code Generation</h1>
17 <!--*********************************************************************-->
18 <p><em>WARNING: This project was part of the Google Summer of Code 2012.
19 It is currently not finished, but it is in the design and implementation stage.
20 The ideas/plans described here may not yet be implemented in Polly and may
21 change later on.</em></p>
22
23 This project adds GPGPU code generation feature to Polly.
24
25 <h2>Objective</h2>
26 <p>The overall objective of this GSoC project is to create a preliminary
27 implementation of GPGPU code generation for Polly. With this addition, users
28 can parallelize some perfectly nested loops with Polly to execute on a
29 heterogeneous platform, composed of CPU and GPU.</p>
30 <p>There are several successful projects about automatic source-to-source gpu
31 code transformation. C-to-CUDA[1] uses the standard Pluto algorithms for
32 computing an affine schedule and then applies a wavefront transformation to
33 obtain one sequential and n-1 parallel loops. The parallel loops are then
34 mapped onto the blocks and threads of GPU. PPCG[2] introduces some advanced
35 algorithms which can expose much more parallelism than other methods . And It
36 also introduces affine partition heuristics and code generation algorithms
37 for locality enhancement in the registers and shared memory.</p>
38 <p>Since automatic GPGPU code generation is quite a complex problem and what we
39 target is a low-level intermediate representation, LLVM IR, rather than a
40 high-level language source, it is important for us to set a proper objective
41 as a start step to give a complete solution to GPGPU code generation for LLVM
42 IR.</p>
43 <p>Firstly, we plan to target two kinds of relatively simple test cases. One is
44 comprised of pure parallel and perfectly nested loops, like the following
45 code.</p>
46 <pre>
47 parfor(int i=0 to M)
48 parfor(int j=0 to N)
49 LoopBody(i, j);
50 </pre>
51 <p>Another one is that all the loops in it are parallel except the inner-most
52 one, just like this:</p>
53 <pre>
54 parfor(int i=0 to M)
55 parfor(int j=0 to N)
56 non-parfor(int k=0 to K)
57 LoopBody(i, j, k);
58 </pre>
59 <p>The LoopBody part should be limited to instructions or functions calls
60 (intrinsics) which can be handled by LLVM's NVPTX backend.</p>
61 <p>On the other hand, we focus on building a preliminary and scalable framework
62 of GPGPU code generation for polly. Thus we plan to employ relatively simple
63 tiling and mapping algorithms and optimize them later.</p>
64 <h2>Work Flow</h2>
65 <h3>GPGPU Code Generation In General</h3>
66 <p>C-to-CUDA[1] and PPCG[2] propose similar steps to solve the automatic GPGPU
67 code generation problem.</p>
68 <li>Look for parallel loops.</li>
69 <li>Create a polyhedral model from the loops.</li>
70 <li>Tile and map the loops to GPU blocks and threads.</li>
71 <li>Determine where to place the data.</li>
72 <h3>What has been done in Polly</h3>
73 <p>Polly has implemented the 1st, 2nd and part of the 3rd of the above steps and
74 many other analysis and transformation passes.</p>
75 <h3>What to do in Polly</h3>
76 <p>Unlike many source-to-source optimizers such as C-to-CUDA and PPCG, Polly is
77 a low-level optimizer, which means we can't use a source-level compiler
78 (e.g. NVCC) to generate the final assembly for the device. We need manually
79 insert device driver API calls to execute the generated kernel assembly
80 text.</p>
81 <p>In this project, we assume that the device driver library has provided an
82 interface to launch kernels in the form of assembly text. Fortunately, most
83 of the mainstream GPU vendors provide such a feature in thier products (see
84 ptxjit of NVIDIA GPUs and CAL of AMD GPUs). Generally speaking, what we
85 are going to do in Polly is:</p>
86 <li>Find a way to tile the parallel loops.</li>
87 <li>Find a way to extract the loop body and transform it into thread-centric
88 parallel code.</li>
89 <li>Find a way to store/load the thread-centric code into/from a device module.
90 <li>Find a way to pass the target machine information and generate code of the
91 device module for the target.
92 <li>Find a way to map the tiled loop to GPU blocks and threads.</li>
93 <li>Find a way to insert CUDA synchronization operations on-demand.
94 <li>Find a way to generate the memory copy operations between a host and a
95 device.</li>
96 <li>Implement/Wrap a runtime library to serve as the execution engine for the
97 generated device code.</li>
98
99 <h3>The Work Flow</h3>
100 <p>In this section, we assume that the host cpu is X86 and the device is NVIDIA
101 CUDA-compatible. we will use the following test case to describe our work
102 flow.</p>
103 <pre>
104 for(i = 0; i &lt; 128; i++)
105 for(j = 0; j &lt; 128; j++)
106 A[i][j] = i*128 + j;
107 </pre>
108 <p>The work flow of our code generator is as follows.</p>
109 <p>1.We first use Polly's jscop file importer to get a wanted 4-level parallel
110 tiled code.</p>
111 The "schedule" part of the pre-optimization jscop file is as the following:
112 <pre>
113 "schedule" : "{ Stmt_for_body3[i0, i1] -&gt; schedule[0, i0, 0, i1, 0] }"
114 </pre>
115 The jscop file describing the tiling transformation is:
116 <pre>
117 "schedule" : "{ Stmt_for_body3[i0, i1] -&gt; schedule[0, o0, o1, o2, o3]:
118 o0 &gt;= 0 and o0 &lt;= 7 and o1 &gt;= 0 and o1 &lt;= 15 and
119 o2 &gt;= 0 and o2 &lt;= 7 and o3 &gt;= 0 and o3 &lt;= 15 and
120 i0 = 16o0 + o1 and i1 = 16o2 + o3 }"
121 </pre>
122 We can test the schedule with the following command line.
123 <pre>
124 opt -load /path/to/polly/build/LLVMPolly.so -basicaa -polly-import-jscop
125 -polly-ast -analyze -q ./test.ll
126 -polly-import-jscop-postfix=transformed+gpu
127 </pre>
128 The output of this schedule is:
129 <pre>
130 for (c2=0;c2&lt;=7;c2++) {
131 for (c3=0;c3&lt;=15;c3++) {
132 for (c4=0;c4&lt;=7;c4++) {
133 for (c5=0;c5&lt;=15;c5++) {
134 Stmt_for_body3(16*c2+c3,16*c4+c5);
135 }
136 }
137 }
138 }
139 </pre>
140 Now we get a 4-dimensional parallel loops with a single SCoP statement in it.
141 <p>2.We then extract the loop body (or the inner-most non-parallel loop) into a
142 LLVM function, tagging it with PTX_Kernel call convention.</p>
143 <p>3.We extract the PTX_kernel function into a temporary module, set the target
144 triple (e.g. nvptx64-unknown-linux) for the module, transform the temporary
145 module into a string, store it in the original module and erase the
146 PTX_kernel function.</p>
147 <p>4.We replace the loops with their GPGPU counterpart. The GPGPU part of code
148 is composed of a call to the llvm.codegen intrinsic and function calls to our
149 GPU runtime library.</p>
150 <p>5.Finally, we generate the executable program with <em>llc</em> or run the
151 optimized LLVM IRs with a JIT compiler like <em>lli</em>.</p>
152 <h2>Usage</h2>
153 <p>1. Apply the llvm.codegen intrinsic patch to LLVM code base.</p>
154 <pre>cd /path/to/llvm/source
155 git am /path/to/polly/source/utils/0001-Add-llvm.codegen-intrinsic.patch</pre>
156 <p>2. Build the test case.</p>
157 <pre>/path/to/polly/source/test/create_ll.sh test.c</pre>
158 <p>3. Get and edit the jscop file (take function "gpu_codegen" as an example).
159 </p>
160 <pre>opt -load /path/to/polly/build/lib/LLVMPolly.so -basicaa
161 -polly-export-jscop ./test.ll
162 cp gpu_codegen___%for.cond---%for.end8.jscop
163 gpu_codegen___%for.cond---%for.end8.jscop.transformed+gpu
164 vi gpu_codegen___%for.cond---%for.end8.jscop.transformed+gpu</pre>
165 <p><em>(Please refer to section "The Work Flow" on how to edit the "schedule"
166 part of a statement)</em></p>
167 <p>4. Optimize the code with GPGPU code generation.</p>
168 <pre>opt -load /path/to/polly/build/lib/LLVMPolly.so -basicaa
169 -polly-import-jscop-postfix=transformed+gpu -enable-polly-gpgpu
170 -polly-gpgpu-triple=nvptx64-unknown-unknown -polly-codegen ./test.ll -S
171 -o test.gpued.ll</pre>
172 <p>5. Build the final assembly and executable.</p>
173 <pre>llc test.gpued.ll -o test.s
174 gcc test.s -lGPURuntime -o test</pre>
175 <p><em>(Please make sure that LD_LIBRARY_PATH is set properly so that
176 /path/to/polly/build/lib/libGPURuntime.so is visible to gcc.)</em></p>
177 <h2>TODO List</h2>
178
179 <table class="wikitable" cellpadding="2">
180 <tbody>
181 <tr style="background: rgb(239, 239, 239)">
182 <th width="400px"> Tasks</th>
183 <th width="150px"> Status </th>
184 <th> Owner </th>
185 </tr>
186 <tr>
187 <th align="left">Tiling the Parallel Loops with An External Jscop File</th>
188 <td align="center" class='open'>Open, In Design</td>
189 <td>Yabin Hu</td>
190 </tr>
191 <tr>
192 <th align="left">GPU Runtime Library Implementation</th>
193 <td align="center" class='inprogress'>Coding Finished, In Reviewing</td>
194 <td></td>
195 </tr>
196 <tr>
197 <th align="left">llvm.codegen Intrinsic Implementation</th>
198 <td align="center" class='inprogress'>Codeing Finished, To Be Reviewed</td>
199 <td></td>
200 </tr>
201 <tr>
202 <th align="left">Code Generation For Host</th>
203 <td align="center" class='inprogress'>50% Done</td>
204 <td></td>
205 </tr>
206
207 </tbody></table>
208
209 <h2>References</h2>
210 <li type="1" value="1">
211 <em>Automatic C-to-CUDA Code Generation for Affine Programs. </em><br />
212 Muthu Manikandan Baskaran, J. Ramanujam and P. Sadayappan.<br />
213 International Conference on Compiler Construction (CC) 2010.<br />
214 </li>
215 <li type="1"><em>PPCG Project</em><br />
216 <a href="http://freecode.com/projects/ppcg">http://freecode.com/projects/ppcg
217 </a></li>
218 <li type="1">
219 <em>Where is the Data? Why You Cannot Debate GPU vs. CPU Performance Without the
220 Answer. </em><br />
221 Chris Gregg and Kim Hazelwood<br />
222 International Symposium on Performance Analysis of Systems and Software
223 (ISPASS) 2011.
224 </li>
225 <p></p>
226 </div>
227 </div>
228 </body>
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