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Word2Vec remains one of the highly-impactful innovations in the field ofNatural Language Processing (NLP) that represents latent grammatical andsyntactical information in human text with dense vectors in a low dimension.Word2Vec has high computational cost due to the algorithm's inherentsequentiality, intensive memory accesses, and the large vocabularies itrepresents. While prior studies have investigated technologies to exploreparallelism and improve memory system performance, they struggle to effectivelygain throughput on powerful GPUs.  We identify memory data access and latency as the primary bottleneck in priorworks on GPUs, which prevents highly optimized kernels from attaining thearchitecture's peak performance. We present a novel algorithm, FULL-W2V, whichmaximally exploits the opportunities for data reuse in the W2V algorithm andleverages GPU architecture and resources to reduce access to low memory levelsand improve temporal locality. FULL-W2V is capable of reducing accesses to GPUglobal memory significantly, e.g., by more than 89\%, compared to priorstate-of-the-art GPU implementations, resulting in significant performanceimprovement that scales across successive hardware generations. Our prototypeimplementation achieves 2.97X speedup when ported from Nvidia Pascal P100 toVolta V100 cards, and outperforms the state-of-the-art by 5.72X on V100 cardswith the same embedding quality. In-depth analysis indicates that the reductionof memory accesses through register and shared memory caching andhigh-throughput shared memory reduction leads to a significantly improvedarithmetic intensity. FULL-W2V can potentially benefit many applications in NLPand other domains.

FULL-W2V: Fully Exploiting Data Reuse for W2V on GPU-Accelerated Systems