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In genome analysis, it is often important to identify variants from a reference genome. However, identifying variants that occur with low frequency can be challenging, as it is computationally intensive to do so accurately. LoFreq is a widely used program that is adept at identifying low-frequency variants. This article presents a design framework for an FPGA-based accelerator for LoFreq. In particular, this accelerator is targeted at virus analysis, which is particularly challenging, compared to human genome analysis, as the characteristics of the data to be analyzed are fundamentally different. Across the design space, this accelerator can achieve up to 120× speedups on the core computation of LoFreq and speedups of up to 51.7× across the entire program. 

An increasing number of applications benefit from heterogeneous hardware accelerators. Such accelerators often require the application to manually manage memory buffers on devices and transfer data between host and device buffers. A programming model that unifies the virtual address space across the host and devices is appealing because it enables automatic memory transfers and simplifies application-level programming. However, the automatic memory transfers can sometimes be redundant, which decreases performance. NVIDIA’s UVM (unified virtual memory) driver provides a unified virtual address space for CPU-GPU programming. This paper identifies redundant memory transfers (RMTs) as a common performance issue with UVM. To address this issue, this paper proposes a data discard directive, and evaluates two implementations of that directive, UvmDiscard and UvmDiscardLazy. This directive exploits application-level knowledge to avoid RMTs. The implementations were integrated with NVIDIA’s open-source UVM driver to demonstrate their usefulness on real-world CUDA UVM applications. For example, the use of the discard directive increases training throughput by 61.2% on a large deep learning application that oversubscribes GPU memory. 

In genome analysis, it is often important to identify variants from a reference genome. However, identifying variants that occur with low frequency can be challenging, as it is computationally intensive to do so accurately. LoFreq is a widely used program that is adept at identifying low frequency variants. This paper presents an FPGA-based accelerator for LoFreq. In particular, this accelerator is targeted at virus analysis, which is particularly challenging, compared to human genome analysis, as the characteristics of the data to be analyzed are fundamentally different. This accelerator can achieve up to 120× speedups on the core computation of LoFreq and speedups of up to 32.4× across the entire program.