Parallelize Gaussian Process Regression MxM double-precision floating point values and run on a single streaming multiprocessor of Nvidia K20 GPU.
The steps to compile and execute the code :
1. module load intel/2017A CUDA
2. nvcc gpr_gpu.cu -o gpr_all_gpu.exe
3. ./gpr_all_gpu.exe 64 0.05 0.05
4. nvcc gpr_gpu_single.cu -o gpr_single_gpu.exe
5. ./gpr_single_gpu.exe 64 0.05 0.05
6. bsub < gpr_gpu.job (the job file is attached in appendix)
As the problem statement required us to use only a single SM, the inter-thread communication would not be an issue. The main performance bottlenecks were identified as the data dependency in solvers and cholesky decomposition. For example, the triangular solver function can not jump to next row until and unless the previous unknown has been computed. And for Cholesky, the entire column/row values of output triangular matrix depends upon diagnonal element and previous columns/rows for Lower Triangular/Upper Triangular respectively. Given these data dependencies, we are restricted to perform these calculations in-order.
There are basically two components for parallelization, sum reductions and data independent computations.
In my implementation, I have used a shared memory array of type double and length equal to the number of threads to store the partial sums which each thread calculates in parallel. In the next step, each thread calls upon the routine to get_total_sum. In this routine, at every step, half as many threads aggregates the partial some from other halves in a stride friendly manner for memory bandwidth optimization. The memory accesses to adjacent locations can be served in parallel giving higher bandwidth. The shared memory access is faster as it is closer to the core. Also, the logic computes the sum only from populated entries avoiding unnecessary calculations.
Though the code refers to L for a Lower triangular matrix, the matrix is populated and stored as Upper Triangular to improve memory bandwidth as accesses can be coalesced among different threads easily. It should be noted that Cholesky factors are transpose of one another. Hence, we can either compute L or L’. As the memory accesses for L’ is better optimized for L’ , I have stored it as L’. This routine goes to each row in L one by one. All threads in a row compute the diagonal element using Sum Reductions. Once the diagonal element is available, the dependency for entire row is complete and all threads start calculating a corresponding element. Again shared memory array is used to store the working sum which improves the memory operations. After the completion of this row, same steps are carried out for the next row as well.
cudaDeviceSetSharedMemConfig(cudaSharedMemBankSizeEightByte) The above configuration for shared memory bank size optimizes memory for double precision accesses and reduce conflicts.
Output using a complete Streaming Processor(196 GPU cores)
Selected m value : 64 The required Rstar value : 2.000000, 2.000000 Input: N = 192, threads_per_block = 192 The predicted value of f at r_star : -0.149718 Elapsed time: Cholesky = 17478.107422 ms Elapsed time: Solver = 128.109604 ms Elapsed time: Kernel = 18134.296875 ms Floating point operations Cholesky Factorization: 24534278144 Floating point operations per second (FLOPS) Cholesky : 1.307311 Gflops Floating point operations Solver: 35135488 Floating point operations per second (FLOPS) Solver: 0.255426 Gflops
Hence, the FLOPS for Cholesky my implementation offers is 1.307311 Gflops for a matrix of dimension 4096x4096. For solver it comes to 0.255426 Gflops for the same dimension.
Peak Flops over SM = (2 x 0.71 x 196) Gflops = 272.32 Gflops (The 2 is for FMA operations)
My Cholesky Implementation is 0.46% of the peak rate and solver is 0.09% of the peak rate.
There are many synchronization calls and presence of divergent threads in my code which eats up most of the performance.
Output using 1 GPU core
Selected m value : 64 The required Rstar value : 2.000000, 2.000000 Input: N = 1, threads_per_block = 1 The predicted value of f at r_star : -0.149718 Elapsed time: Cholesky = 2261328.000000 ms Elapsed time: Solver = 7504.510742 ms Elapsed time: Kernel = 2347107.500000 ms Floating point operations Cholesky Factorization: 22931662848 Floating point operations per second (FLOPS) Cholesky : 0.009444 Gflops Floating point operations Solver: 33570816 Floating point operations per second (FLOPS) Solver: 0.004166 Gflops
The speed-up/efficiency obtained on 196 cores over a single core :
| Single_core | Full SM | Speed-up | Efficiency | |
|---|---|---|---|---|
| Cholesky | 2261328 | 17478 | 129.38 | 66.01% |
| Solver | 7504 | 128 | 58.63 | 29.91% |
| Total | 2347107 | 18134 | 129.43 | 66.04% |