Computational Genomics Scaling with GPU and FPGA Parallelization
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Solution Overview
Problem
Current computational genomics methods are inefficient and costly when processing large datasets, such as those in genome-wide association studies and quantitative trait loci mapping, due to the high computational demands of central processing unit (CPU) based methodologies, which are not scalable to handle millions of samples.
Innovation Solution
Implementing computational genomics algorithms on specialized architectures like graphics processing units (GPUs), tensor processing units (TPUs), and field programmable gate arrays (FPGAs) for highly parallelized computations, leveraging libraries such as PyTorch and TensorFlow to accelerate and scale analyses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If CPU-based methodologies are used for computational genomics, then the system is easier to implement and maintain, but the computational speed and efficiency are insufficient for processing millions of samples
Solution Approach 1:
The patent segments the computational workload by dividing it between CPU and GPU resources. The CPU handles data preprocessing and coordination tasks, while the GPU performs the computationally intensive parallel calculations. This segmentation allows the system to leverage the strengths of each processor type, achieving high computational speed without requiring a complete architectural overhaul.
Solution Approach 2:
The patent introduces CUDA kernels as an intermediary layer that enables communication and data transfer between the CPU and GPU. This intermediary mechanism facilitates efficient parallel processing by allowing the CPU to offload specific computational tasks to the GPU while maintaining system-level coordination, thus improving productivity without excessive complexity.
2Productivity
If large-scale CPU clusters are deployed to handle millions of samples, then computational capacity increases, but the cost and computational time remain prohibitively high
Solution Approach 1:
The patent replaces the traditional sequential CPU-based computational mechanism with a parallel GPU-based mechanism. By utilizing the GPU's architecture designed for simultaneous execution of multiple operations, the system achieves orders of magnitude improvement in processing capacity while reducing computational time from days to hours or minutes for large-scale genomic analyses.
Solution Approach 2:
The patent creates a hybrid computational system that combines CPU and GPU resources into a composite architecture. This composite approach leverages the CPU's control capabilities and the GPU's parallel processing strength, achieving high productivity without the prohibitive costs and time requirements of pure CPU scaling.
3Productivity
If specialized architectures like GPUs, TPUs, or FPGAs are used for highly parallelized computations, then computational speed increases by orders of magnitude, but the device complexity and implementation difficulty increase
Solution Approach 1:
The patent implements a universal computational framework using CUDA kernels that can be applied across different GPU architectures and genomics applications. This multi-functional approach allows the same codebase to run on various NVIDIA GPU models and different genomic analysis tasks, reducing implementation difficulty despite the use of specialized hardware.
Solution Approach 2:
The patent utilizes parameter changes in the form of configurable CUDA kernel parameters that allow the same computational algorithm to adapt to different data sizes, GPU memory capacities, and hardware configurations. This flexibility simplifies implementation by allowing users to adjust parameters rather than rewrite code for different hardware scenarios.
Data Source
AI summary
The disclosure relates to methods for increasing the speed and efficiency of computational genomics. In particular, the disclosure relates to methods of scaling computational genomics by using one or more specialized architectures for highly parallelized computations, such as graphics processing units (GPUs), tensor processing units (TPUs), and field programmable gate arrays (FPGAs), and the like, to compute the computational genomics calculations.