Can this be done on my laptop, or do I require HPC resources?
A few years ago, I vividly remember my colleagues and I striving to upgrade our laptops and personal computers (PCs). We went as far as forgoing various indulgences to save for new ones with top-notch hardware specifications. Our aim was simple: a laptop that could perform most, if not all, types of bioinformatics analyses, covering as many permutations as possible. This aspiration was, of course, limited by our purchasing power, which we could only leverage a little as students. We would later learn that we were aiming at a moving target.
With the rapidly evolving bioinformatics and computational biology space, the researchers developed superior computational infrastructure and even greater software enhancements. With the upgrades, we must consider factors like storage space, efficiency, reproducibility, and scalability while minimising costs. Therefore, scientists face a critical decision: can they accomplish their computational tasks on a personal laptop, or do they need the enhanced power of a high-performance computing (HPC) environment? Let’s delve into this dilemma and explore some factors you can consider to drive your decision-making process.
1. The Challenge of Computational Intensity
Bioinformatics is steadily evolving into more specialised sub-fields, ranging from classical alignment tasks to genome assembly utilising robust algorithms, protein structure prediction, and multi-omics analyses, each posing distinct computational challenges. While a laptop can comfortably handle some analyses, others, such as large-scale assembly projects, highly optimised pipelines with several dependencies, and various multimodal models integrating different data types, require the might of an HPC environment to tackle the computational intensity effectively.
2. The Moving Target of Increasing Data Sizes and Hardware Requirements
With drastically improving computational infrastructure, compute power and capabilities, as do dataset sizes, continue to increase considerably. With this exponential increase comes the challenge of storing and analysing the data. Some good examples include large genomic datasets, high-resolution imaging data, and various multi-omics datasets that have continued to outgrow the storage and memory capacities of a typical laptop.
Don’t get me wrong; we have witnessed an immense transformation in the capabilities of personal computers in the recent past to levels beyond imagination. Consider arguably two of the most advanced laptops: the MacBook Pro 16-inch (M3, 2023) and the ASUS ProArt Studiobook OLED (H7604). The MacBook Pro boasts a formidable 12/16-core CPU and an 18–40-core GPU on its M3 chip, complemented by up to 96 GB of RAM and a capacious 8TB SSD. Similarly, the ASUS ProArt Studiobook OLED (H7604) offers an Intel Core i9–13980HX chip paired with a cutting-edge NVIDIA GeForce RTX 4070 graphics card, along with up to 64 GB of RAM and an equally impressive 8TB SSD.
These PCs are capable of performing most bioinformatics analyses. However, the price tag of these state-of-the-art laptops is costly, as both laptops retail for a few thousand dollars. While one might argue that this cost is a one-time investment, it’s crucial to consider the additional expenses associated with ensuring that the PC infrastructure aligns with the analysis software requirements. These expenses include software installation, upkeep, maintenance, and storage costs. In essence, even if you managed to find a laptop capable of meeting your analysis needs, the overall expenses and optimisation time would be prohibitively high.
3. Software Requirements
Hardware and software are joined at the hip, as the choice of one determines the limits of another. The software tools and packages used in bioinformatics often determine the computational resources needed for analysis. Some applications require vast computational power or specialised hardware support, which renders them impractical for execution on a laptop. The transition to an HPC environment equipped with optimised software stacks will likely unlock these tools’ full potential and enable researchers to tackle complex analyses easily.
Additionally, most HPC resources benefit from being professionally managed by dedicated software personnel, relieving bioinformaticians of the burdens of software installation and accompanying dependencies, optimisation, and storage management. As a bioinformatician, you have more time to focus on analysis rather than on the intricacies of hardware and software.
4. Harnessing the Power of Parallelization
Parallel computing lies at the heart of many bioinformatics analyses, offering a pathway to faster and more efficient data processing. Parallelisation enables dividing large computational tasks into smaller tasks concurrently executed on multiple processors or cores to reduce overall computation time. Tasks such as sequence alignment, variant calling, and phylogenetic reconstruction can benefit immensely from parallelisation, achieved in an HPC environment with multiple processors or GPUs. By harnessing the power of parallel computing, researchers can significantly accelerate their analyses and unlock new insights from their data, another optimal use case of HPCs over a personal laptop.
5. Time Sensitivity and Efficiency
Research has become more fast-paced in a highly competitive and dynamic world, making time an invaluable resource. With faster processing speeds and parallel computing capabilities, HPCs offer more value to researchers, enabling them to accelerate their analyses and meet tight deadlines. By leveraging the efficiency provided by HPC resources, researchers can gain a competitive edge, accelerate scientific discoveries, and stay ahead in the race against time.
6. Balancing Costs and Benefits
While accessing HPC resources may entail upfront costs or institutional access requirements, their computational power, scalability, and efficiency benefits often outweigh the investment. Compared to purchasing high-end hardware for personal use, leveraging HPC resources can be more cost-effective for large-scale or time-sensitive analyses, making it a prudent choice for researchers seeking to maximise their resources.
7. Future-Proofing and Collaboration
As bioinformatics projects grow in scale and complexity, the need for scalable and collaborative computing solutions becomes increasingly apparent. HPC environments provide the infrastructure and tools necessary to support collaborative research efforts from multiple fronts, facilitate data management, and ensure scalability and reproducibility as projects evolve. By embracing HPC resources, researchers can future-proof their analyses, foster collaboration, and propel their research to new boundaries.
In conclusion, the decision to perform bioinformatics analyses on a laptop or in an HPC environment hinges on various factors, including computational intensity, data size, software requirements, and time sensitivity. By carefully considering these factors, researchers will likely save time and money, unlock new possibilities, accelerate scientific discoveries, and pave the way for groundbreaking advancements and collaborations.
At Jenomu Bioinformatics, our team is passionate about helping researchers and professionals make the most of their data and attain their research objectives. Our commitment is to support your research endeavours by providing access to bioinformatics experts and optimal computational resources. Partnering with us accelerates your research progress, enhances efficiency, and minimises the time and expenses typically required for acquiring and configuring computational infrastructure. Reach out today to get started.
