gpuMCMCProcessorUtils.cuh

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#pragma once
// MaCh3 utils for processing/diagnostic MCMC
// Written by Kamil Skwarczynski
//
// Contains code to run on CUDA GPUs. Right now only can calculate autocorrelations
// Potential extensions:
// -Covariance matrix calculations and other matrix operations
// -Effective Sample Size evaluation
 
#include "Manager/gpuUtils.cuh"
 
 
__global__ void EvalOnGPU_AutoCorr(
  const float* __restrict__ ParStep_gpu,
  const float* __restrict__ ParamSums_gpu,
  float*  NumeratorSum_gpu,
  float*  DenomSum_gpu);
 
 
class MCMCProcessorGPU
{
 public:
  MCMCProcessorGPU();
  virtual ~MCMCProcessorGPU();
 
  __host__ void InitGPU_AutoCorr(
                            int n_Entries,
                            int n_Pars,
                            const int n_Lags);
 
  __host__ void CopyToGPU_AutoCorr(
                              float *ParStep_cpu,
                              float *NumeratorSum_cpu,
                              float *ParamSums_cpu,
                              float *DenomSum_cpu);
 
  __host__ void RunGPU_AutoCorr(float*  NumeratorSum_cpu,
                                float*  DenomSum_cpu);
 
  __host__ void CleanupGPU_AutoCorr();
 
 
 private:
  float* ParStep_gpu;
  float* NumeratorSum_gpu;
  float* ParamSums_gpu;
  float* DenomSum_gpu;
 
  // h_NAME declares HOST constants (live on CPU)
  int h_nLag;
  int h_nDraws;
  int h_nEntries;
};