KernelGen - GPU Technology Conference

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KernelGen compiler pipeline.KernelGen conserves original host compiler pipeline (based on GCC), extending it with parallelLLVM-based pipeline, which is activated and/or used if specific environment variables are set.1. Compile-time1.1. Generate binary CPU code1.2. Generate LLVM IR code1.3. Branch loops into separatefunctions in LLVM IR1.4. Embed LLVM IR for loopsinto object file2. Link-time2.1. Load LLVM IR form objects2.2. Extract main entry intoseparate LLVM IR module2.3. Resolve (link) dependenciesin LLVM IR code2.4. Embed LLVM IR modules forloops and main entry into executablebinary3. Run-time3.1. Load LLVM IR from binary3.2. Load external LLVM IR formath and workflow control3.3. Optimize, codegen & launchGPU kernel for main entry,from LLVM IR3.4. Analyze, optimize, codegen& launch GPU kernels forGPU-efficient loops, from LLVM IR3.5. Handle CPU host calls38 / 75
GPU-directed execution.GPUdataon GPUCPU.idleserialcodeparallelcodeserialcodeserialcodeinitidlecallbackcallbackcallbackhostcallcallbackKernelGen uses GPU-directed execution model:All activities are initiated by GPU (CPU is “passive”)All data (global, dynamic, immediate) resides GPUmemoryGPU data and control flow are managed by main kernel,which is persistent on GPU during whole applicationlifetimefinalize39 / 75
Page 4 and 5: Simple example: Fortran.Compile the
Page 6 and 7: Simple example: C.Compile the Fortr
Page 8 and 9: OpenACC: no external calls.OpenACC
Page 10 and 11: KernelGen: and external static libr
Page 12 and 13: KernelGen: smart pointers tracking.
Page 14 and 15: KernelGen: smart pointers tracking.
Page 16 and 17: Other nice features..Comfortable ac
Page 18 and 19: Parallelizing Fortran array-wise co
Page 20 and 21: OpenACC: no pointer arithmetics.Com
Page 24 and 25: absolute speedup against PGI versio
Page 26 and 27: Performance factors: compute grid.2
Page 28 and 29: Performance factors: code optimizat
Page 30 and 31: Performance factors: code generatio
Page 32 and 33: absolute speedup against CPU versio
Page 34 and 35: Parallelism detection.KernelGen ana
Page 36 and 37: Cooperation with MPI..KernelGen nat
Page 40 and 41: GPU-directed execution.GPUidleseria
Page 42 and 43: KernelGen execution workflow.Scenar
Page 62 and 63: Code generation brief walk-through.
Page 70 and 71: Conclusions.1 KernelGen project imp
Page 72 and 73: Testing COSMO and WRF.KernelGen is
Page 74 and 75: Collaboration proposal.KernelGen te

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