most citedSaLoBa: Maximizing Data Locality and Workload Balance for Fast Sequence Alignment on GPUs

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

cs.DC2024

PID-Comm: A Fast and Flexible Collective Communication Framework for Commodity Processing-in-DIMM Devices

Si Ung Noh, Junguk Hong, Chaemin Lim +5

Recent dual in-line memory modules (DIMMs) are starting to support processing-in-memory (PIM) by associating their memory banks with processing elements (PEs), allowing application…

cs.DC2024

AGAThA: Fast and Efficient GPU Acceleration of Guided Sequence Alignment for Long Read Mapping

Seongyeon Park, Junguk Hong, Jaeyong Song +3

With the advance in genome sequencing technology, the lengths of deoxyribonucleic acid (DNA) sequencing results are rapidly increasing at lower prices than ever. However, the longe…

eess.AS2023

Automatic Tuning of Loss Trade-offs without Hyper-parameter Search in End-to-End Zero-Shot Speech Synthesis

Seongyeon Park, Bohyung Kim, Tae-hyun Oh

Recently, zero-shot TTS and VC methods have gained attention due to their practicality of being able to generate voices even unseen during training. Among these methods, zero-shot…

eess.AS2023

Unsupervised Pre-Training For Data-Efficient Text-to-Speech On Low Resource Languages

Seongyeon Park, Myungseo Song, Bohyung Kim +1

Neural text-to-speech (TTS) models can synthesize natural human speech when trained on large amounts of transcribed speech. However, collecting such large-scale transcribed data is…

cs.DB20231 cited

SaLoBa: Maximizing Data Locality and Workload Balance for Fast Sequence Alignment on GPUs

Seongyeon Park, Hajin Kim, Tanveer Ahmad +5

Sequence alignment forms an important backbone in many sequencing applications. A commonly used strategy for sequence alignment is an approximate string matching with a two-dimensi…