{
  "template_for_generator": [
    {
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        "Novel", 
        "New", 
        "Reproducible"
      ]
    }, 
    {
      "choice": [
        "technique", 
        "approach", 
        "tool", 
        "infrastructure", 
        "framework", 
        "methodology"
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      "text": " "
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    {
      "choice": [
        "holistic", 
        "global", 
        "universal", 
        "open", 
        "systematic", 
        "realistic", 
        "practical"
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      "text": " for "
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    {
      "choice": [
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        "multi-objective", 
        "multi-dimensional", 
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        "predictive analytics based", 
        "big data based", 
        "active learning based", 
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        "KNN-based", 
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        "code pattern based", 
        "probabilistic", 
        "stochastic", 
        "reactive", 
        "phase-based", 
        "hardware counters based"
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      "probability": 0.5, 
      "text": " and "
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    {
      "choice": [
        "benchmarking", 
        "model/sw/hw co-design", 
        "benchmarking", 
        "auto-tuning", 
        "iterative compilation", 
        "behavior modeling", 
        "optimization space exploration", 
        "design space exploration", 
        "feature selection", 
        "feature detection", 
        "algorithm tuning", 
        "compiler optimization", 
        "run-time adaptation", 
        "dynamic adaptation", 
        "scheduling", 
        "parallelization", 
        "SW/HW co-design"
      ], 
      "text": " "
    }, 
    {
      "choice": [
        "improve performance", 
        "improve accuracy", 
        "reduce energy usage", 
        "reduce code size", 
        "reduce optimization time", 
        "balance multiple characteristics", 
        "select optimal accuracy, speed, throughput, latency, energy, costs on a Pareto frontier", 
        "balance algorithm accuracy, power consumption and speed", 
        "reduce power consumption", 
        "improve reliability", 
        "reduce errors", 
        "reduce usage costs", 
        "balance performance and energy"
      ], 
      "probability": 0.4, 
      "text": " to "
    }, 
    {
      "choice": [
        "quantum computers", 
        "Intel MIC processors", 
        "Intel AI processors", 
        "AI processors", 
        "ARM processors", 
        "GPGPUs", 
        "accelerators", 
        "TPU", 
        "DSP", 
        "FPGAs", 
        "DNN accelerators", 
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        "neural network accelerators", 
        "architecture simulator", 
        "AMD processors", 
        "NVidia GPU processors", 
        "STMicroelectronics processors", 
        "architecture simulator", 
        "multi-core architectures", 
        "many-core architecture", 
        "heterogeneous systems", 
        "reconfigurable systems"
      ], 
      "probability": 0.2, 
      "text": " for "
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    {
      "choice": [
        "mobile phones", 
        "IoT devices", 
        "tablets", 
        "laptops", 
        "exascale supercomputers", 
        "supercomputers", 
        "HPC servers", 
        "GRID systems", 
        "cloud services", 
        "data centers"
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      "probability": 0.2, 
      "text": " in "
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    {
      "choice": [
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        "LLVM", 
        "GCC", 
        "ICC", 
        "XLA compiler", 
        "XLC compiler", 
        "Java", 
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        "ROSE source-to-source compiler", 
        "Polly", 
        "JIT compiler", 
        "source-to-source compiler"
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      "probability": 0.2, 
      "text": " using "
    }, 
    {
      "choice": [
        "polyhedral transformations", 
        "inlining", 
        "unrolling", 
        "tiling", 
        "prefetching", 
        "OpenCL", 
        "OpenMP", 
        "CUDA", 
        "MPI", 
        "thread pinning", 
        "contention aware scheduling", 
        "binary to binary transformation", 
        "loop fusion/fission"
      ], 
      "probability": 0.1, 
      "text": " with "
    }, 
    {
      "choice": [
        "exhaustive search", 
        "focused search", 
        "probabilistic search", 
        "random search", 
        "genetic search", 
        "decremental analysis", 
        "hill climbing", 
        "adaptive sampling", 
        "crowdsourcing"
      ], 
      "probability": 0.4, 
      "text": " via "
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    {
      "choice": [
        "diverse", 
        "multiple", 
        "10", 
        "100", 
        "1000"
      ], 
      "probability": 0.2, 
      "select_with_next": "yes", 
      "text": " with "
    }, 
    {
      "choice": [
        "image classification algorithms", 
        "deep learning algorithms", 
        "BLAS routines", 
        "SLAM algorithms", 
        "SPEC benchmarks", 
        "MLPerf benchmarks", 
        "kernels", 
        "microbenchmarks", 
        "codelets", 
        "workloads", 
        "programs", 
        "datasets", 
        "benchmarks"
      ], 
      "text": " "
    }, 
    {
      "text": "."
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  ]
}

{}