PATENT CLAIM ANALYSIS

Application Number: 15924029
Application Type: Utility
Filing Date: 2018-03
Publication Date: 2018-07
Patent Classification: ["345", "506000"]

Abstract:
A method for optimization of machine learning (ML) workloads on a graphics processor unit (GPU). The method includes identifying a computation having a generic pattern commonly observed in ML processes. Hierarchical aggregation spanning a memory hierarchy of the GPU for processing is performed for the identified computation including maintaining partial output vector results in shared memory of the GPU. Hierarchical aggregation for vectors is performed including performing intra-block aggregation for multiple thread blocks of a partial output vector results on GPU global memory.

Claim (Index 4):
The method of  claim 1 , wherein the GPU performs processing comprising:\n receiving an input matrix comprising a sparse matrix of row and column data; partitioning a block of threads, by a thread processor, into the vectors comprising sets of cooperating threads for processing the input matrix; and processing multiple threads in each vector on a same row of the input matrix simultaneously to determine the partial output vector results, wherein:\n performing hierarchical aggregation for the vectors further comprises:\n performing intra-block aggregation or inter-vector aggregation for the vectors via atomic operations; and \n \n performing the intra-block aggregation for the multiple thread blocks of the partial output vector results on the GPU global memory reduces atomic writes to the global memory and provides final output vector results.

Metadata:
- Claim Count in Document: 43.0
- Percentile: 90.0
- Lexical Diversity: 1.71698
- Patent Class: 345.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['14813522', '15190073', '14580110', '14927428', '12569942']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.6154502938565346
- 35 USC 102 Novelty (BERT): 0.5324336737087207
- Combined Prediction Score: 0.6071486318417533
- Mean Citation Score: 209.00097800000003
- Max Citation Score: 368.76996
- Similarity Product: 364.3234125020027

Labels:
- Claim Label 101: 1
- Claim Label 102: 1
- Claim Label 103: 1
- Claim Label 112: 1
- Combined Label: 1
- Label 101 Adjusted: 1

Dataset: test