1 citations · 1 across the 4 of their papers we have counts for
5 papers
Accelerating Machine Learning Primitives on Commodity Hardware
Roman Snytsar
Sliding Window Sum algorithms have been successfully used for training and inference of Deep Neural Networks. We have shown before how both pooling and convolution 1-D primitives c…
Sliding Window Sum Algorithms for Deep Neural Networks
Roman Snytsar
Sliding window sums are widely used for string indexing, hashing and time series analysis. We have developed a family of the generic vectorized sliding sum algorithms that provide…
De(con)struction of the lazy-F loop: improving performance of Smith Waterman alignment
Roman Snytsar
Striped variation of the Smith-Waterman algorithm is known as extremely efficient and easily adaptable for the SIMD architectures. However, the potential for improvement has not be…
Parallel approach to sliding window sums
Roman Snytsar, Yatish Turakhia
Sliding window sums are widely used in bioinformatics applications, including sequence assembly, k-mer generation, hashing and compression. New vector algorithms which utilize the…
Vectorized Character Counting for Faster Pattern Matching
Roman Snytsar
Many modern sequence alignment tools implement fast string matching using the space efficient data structure called FM-index. The succinct nature of this data structure presents un…