FPGA-Based Near-Memory Acceleration of Modern Data-Intensive Applications (2106.06433v2)

Abstract: Modern data-intensive applications demand high computation capabilities with strict power constraints. Unfortunately, such applications suffer from a significant waste of both execution cycles and energy in current computing systems due to the costly data movement between the computation units and the memory units. Genome analysis and weather prediction are two examples of such applications. Recent FPGAs couple a reconfigurable fabric with high-bandwidth memory (HBM) to enable more efficient data movement and improve overall performance and energy efficiency. This trend is an example of a paradigm shift to near-memory computing. We leverage such an FPGA with high-bandwidth memory (HBM) for improving the pre-alignment filtering step of genome analysis and representative kernels from a weather prediction model. Our evaluation demonstrates large speedups and energy savings over a high-end IBM POWER9 system and a conventional FPGA board with DDR4 memory. We conclude that FPGA-based near-memory computing has the potential to alleviate the data movement bottleneck for modern data-intensive applications.

• The paper presents a comprehensive review of read mapping acceleration techniques combining algorithmic and hardware-based strategies.
• It details algorithmic enhancements such as efficient indexing, pre-alignment filtering, and dynamic programming for rapid genomic alignment.
• It advocates the integration of FPGA and in-memory processing solutions to address computational bottlenecks and scalability challenges.

Accelerating Genome Analysis: A Review of Current Approaches in Read Mapping

This paper titled "Accelerating Genome Analysis: A Primer on an Ongoing Journey" provides an exhaustive overview of efforts to optimize and accelerate the read mapping step in genome analysis. It elucidates the inherent bottlenecks introduced by the disparity between genome sequencing capabilities and computational analysis methodologies. The read mapping, a critical phase in genomics, involves aligning sequenced fragments or reads against a reference genome—a task that owes its complexity to both the scale of genomic data and the occurrence of insertions, deletions, and substitutions in DNA sequences.

The research outlines multiple strategies to address these challenges, categorizing them into algorithmic refinements and hardware-based acceleration techniques. The paper makes a compelling case for optimizing read mapping using both software and hardware innovations, detailing the state-of-the-art methodologies in each category.

Algorithmic Enhancements

Efforts in algorithmic enhancement focus on reducing the time complexity of read mapping. The paper discusses various steps:

1. Indexing: Utilization of data structures such as FM-index to store compressed representations of genomic segments, lowering the memory footprint and accelerating seed queries. Tools like minimap2 and methods including seed minimizers significantly optimize for storage and speed.
2. Pre-Alignment Filtering: Introducing heuristic-based filtering mechanisms to swiftly eliminate unlikely site matches, thereby reducing the overall computation. Approaches such as pigeonhole principle filtering, base counting, and q-gram filtering effectively decrease the number of sequences subjected to exhaustive alignment.
3. Sequence Alignment: Enhancement primarily through fast, parallel processing frameworks using dynamic programming approaches. Techniques lever additional compute capabilities by employing SIMD-capable CPUs, GPUs, and specialized hardware like FPGAs and ASICs to efficiently tackle the alignment task.

Hardware-Based Accelerations

Hardware innovations aim to bridge the performance gap by leveraging state-of-the-art computing architectures:

• FPGA and ASIC Designs: Architectures such as SillaX implement parallel processing capabilities tailored for genomic data, optimizing specific operations in the read mapping flow.
• Processing-in-Memory (PIM): Solutions like RAPID perform computational tasks within memory units, substantially minimizing data transfer overhead and power consumption.

Implementation Challenges and Future Directions

Despite significant advancements, challenges persist that require attention. The paper highlights four key impediments: the holistic acceleration of entire genome analysis processes, the substantial data transfer costs within and between systems, the need for flexible and scalable hardware solutions, and the inefficiency of current genomic data formats with respect to emerging sequencing technologies.

The authors speculate that addressing these challenges may catalyze new developments in genomics, including personalized medicine and real-time disease surveillance. Emphasizing hardware/software co-design and in-memory computing paradigms presents a promising path forward.

The ongoing efforts to refine read mappers using both algorithmic and hardware enhancements illustrate the complex interplay of computation and data handling in modern genomics. The insights provided encourage continued innovation towards ubiquitous, rapid, and accurate genomic analysis. The paper serves as a comprehensive survey of the current landscape, charting potential trajectories for future research and development in accelerating read mapping and genome analysis.