Accelerating foundational model training: a systematic review of hardware, algorithmic, and distributed computing optimizations

HIGHLIGHTS

• What: The investigation reveals that a synergistic approach combining specialized hardware accelerators (TPUs/GPUs) with advanced algorithmic techniques including sparse modeling and adaptive optimization can reduce training time by up to 67% compared to traditional methods. The authors demonstrate that implementing mixed-precision training alongside pipeline parallelism and optimal checkpointing strategies yields particularly promising results achieving a 3.2x speedup while maintaining model accuracy within 0.5% of baseline performance. The article presents a systematic evaluation of various acceleration techniques` scalability and cost-effectiveness providing practical guidelines for researchers and practitioners in the field of artificial intelligence . . .
  

   

  

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