Parallel String Match Algorithm In MPI, OpenMP, CUDA Or So

Parallel String Match algorithm - in MPI, OpenMP, CUDA or some

The assignment entails designing a parallel string matching algorithm to locate a specific sequence within a genome, represented by a large file of base pairs, utilizing the computational resources of a high-performance cluster. The goal is to implement an efficient, scalable approach considering the cluster's hardware architecture and limitations, and to estimate its performance metrics in terms of complexity, communication costs, and acceleration relative to a single-processor setup.

Given the problem scope, the key aspects involve choosing an appropriate parallel program model (MPI, OpenMP, CUDA, or hybrid), detailing data partitioning and transfer mechanisms, selecting resource utilization strategies, and estimating performance efficiencies.

Design of the Parallel String Matching Algorithm

The proposed algorithm adopts a hybrid parallel model combining MPI for inter-node communication and CUDA for intra-node GPU acceleration. CUDA is well-suited for data-parallel tasks such as string matching due to its ability to execute thousands of threads concurrently, enabling the processing of large genome segments efficiently.

The algorithm proceeds as follows: initially, the genome data and the query sequence are stored in files on the NFS disk. The data is partitioned among MPI processes, each assigned a segment of the genome file, with overlaps at segment boundaries to manage matches that occur near split points. These segments are transferred from disk to each process’s memory (disk-to-memory transfer), possibly using asynchronous I/O to overlap transfer and computation.

Each process deploys CUDA kernels to perform a parallel pattern match, typically via an adaptation of the Knuth-Morris-Pratt (KMP) or Boyer-Moore algorithm, optimized for GPU execution. The query sequence is broadcast to all processes and to GPU kernels, which then scan their respective genome segments to find occurrences.

The tasks are primarily data parallel: each GPU thread examines one position in the segment for potential matches, with some control logic to handle overlapping regions. The process parallelism comes from multiple MPI processes working on disjoint genome segments, while the data parallelism derives from GPU threads. This hybrid approach allows both process-level distribution (task parallelism across nodes) and thread-level acceleration within nodes (data parallelism within GPUs).

Data Transfer and Resource Usage

The initial hierarchical data transfer involves reading genome and query files from the NFS system into each node's local memory (disk-to-memory), optimized via asynchronous I/O to hide latency. Once in memory, data segments are transferred to each node’s GPU memory for fast parallel access.

Within nodes, CUDA kernels operate on the data residing in GPU RAM (4 GB per GPU). The GPU streams facilitate overlapping data transfer (host to device) with execution, minimizing idle times. Inter-node communication overlays MPI messaging, primarily used to gather results, synchronize tasks, or handle boundary overlaps. Due to the cluster configuration, process-to-process communication may be limited to message passing over Ethernet, constrained by latency (~20 μs) and bandwidth (100 MB/sec for large messages).

Resource utilization involves leveraging all available GPUs across nodes for maximal parallelism, assuming the problem size exceeds the combined GPU memory, which favors using the cluster’s collective computational power. The CPUs provide control, coordination, I/O management, and any residual serial tasks. The network bandwidth governs the efficiency of MPI communications, especially when exchanging boundary results or aggregating final match counts.

Performance Estimation

a. Complexity and Communication Costs

The algorithm’s computational complexity per process is approximately O(N/P), where N is the total genome length and P the number of processes, assuming ideal distribution. GPU kernels operate in parallel, significantly reducing per-process runtime. Communication costs are primarily due to boundary data exchanges (to handle matches crossing segment boundaries) and result collection via MPI messages. Each MPI message incurs latency (~20 μs) plus transfer time proportional to message size and network bandwidth. For large genomes (>1 GB), the dominant cost is computation; for smaller sizes, communication overhead can eclipse computation time, reducing efficiency.

b. File Size and Efficiency

If the genome file is too small (e.g., smaller than the total GPU memory or less than a few megabytes), the overhead of data transfer and process synchronization may outweigh the benefits of parallelism, rendering the approach inefficient. For very tiny genomes, a serial scan on a single CPU may outperform parallel strategies due to minimal overhead. Conversely, as genome size grows into gigabytes or terabytes, the parallel method becomes increasingly advantageous, with an optimal performance window likely between hundreds of megabytes to several gigabytes, where the computational workload sufficiently justifies parallel execution.

c. Speedup Expectations

Assuming ideal scaling, the speedup (S) can be estimated as approximately the number of GPUs utilized, i.e., about 20 nodes × 2 GPUs each = 40 GPUs in total. Theoretically, the maximum speedup approaches 40x over a single CPU, given perfect workload distribution and negligible communication overhead. Realistically, factoring in communication costs and GPU kernel efficiency, a conservative estimate might be around 20-30x acceleration. Empirical studies (Chen et al., 2018; Lee et al., 2020) demonstrate that GPU-accelerated genome searches can achieve this level of speedup, especially when optimized for memory access patterns, thread synchronization, and workload balancing.

Conclusion

The designed hybrid parallel string matching algorithm leverages MPI for inter-node task distribution and CUDA for intra-node data parallelism, effectively handling large-scale genome data. By partitioning data carefully, minimizing communication overhead, and exploiting GPU acceleration, this approach offers significant performance improvements over serial methods. Its efficiency depends on the size of the genome data relative to hardware resources, with optimal performance in large datasets typical of modern genomic applications. Proper tuning of data transfer, workload partitioning, and communication strategies is critical to realize these gains on the described high-performance cluster.

References

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