High-Performance Computing: the bottleneck

High Performance Computing (HPC) has established itself as a fundamental pillar in the technological field, addressing large-scale and high-speed problems unattainable through traditional computing methods. However, this advanced capability comes with its own challenges, particularly the emergence of bottlenecks in data processes.

The problem initially arises with handling large volumes of data. For example, processing a 1TB text data set may be simple, but the scenario changes drastically when the data volume increases to 1 PB or more, and the complexity of the mathematical problems involved intensifies.

An initial solution would be to load the data serially into the computer’s memory for processing. However, here comes the first major obstacle: the storage speed compared to the memory and the CPU. This speed difference between components implies that the system’s performance will be limited by the slowest component.

To address this challenge, an alternative is to divide the data set into smaller parts and distribute them across different computers for parallel analysis and resolution. However, this approach assumes that the data can be split without losing meaning and that the execution can be parallel without altering the final result. Additionally, it requires the integration of a tool that orchestrates both data splitting and simultaneous management of multiple computers.

Another challenge arises with the need for physical and logical interconnection between multiple computers to handle the workload. The network introduces additional reliability and performance issues. For example, data loss or delay in the network can severely affect parallel execution.

In scenarios where large data sets include structural dependencies, complexity increases. Data processing will require an intelligent governor to manage and organize them optimally. Without this, even with sufficient storage and computing resources, they may not be used efficiently or correctly.

In parallel to these data and network problems, there are inherent physical challenges in individual computers designed for high-performance operations. Heat generation in the CPU and limitations in the size of physical memory modules and local storage systems play a crucial role in determining how large data sets can be processed.

An alternative approach could be the use of multiple computers but treating them as a single entity. However, this introduces the concept of Mean Time Between Failures (MTBF), a critical value in HPC due to its parallelized nature. The possibility of failure in a clustered computing system is significantly higher than in individual machines. For example, the failure of a single hard drive in a cluster can cause a critical system failure. This risk increases with the size and amount of data that needs to be analyzed in small parts in numerous instances of parallel computing.

With supercomputing clusters, the MTBF can be as low as several minutes. If the required runtime to solve tasks exceeds this value, mechanisms must be included to ensure that the potential loss of individual components does not affect the system as a whole. However, each added safeguard introduces additional cost and possible performance penalties.

Although parallel execution may seem overly complex, in practice, it is the only viable method to solve some large-scale mathematical problems. The physical limits of individual processors and cooling considerations become critical factors, dictating the performance that a single processor can achieve.

Ultimately, the optimal solution may be the use of multiple computers treated as a single unified entity, but this poses its own unique challenges. The need for a data programmer becomes essential. This programmer must be fast enough to not introduce performance penalties, aware of the infrastructure topology in which it operates, and capable of scaling as needed.

To sum up, there are no shortcuts in High-Performance Computing. Each HPC configuration is unique and requires a different approach. At Canonical, we understand the enormous challenges faced by HPC professionals and want to help simplify some of the core problems in the world of HPC, making HPC software and tools more accessible, easy to use, and maintain. Our goal is to optimize the workloads of HPC customers and reduce their costs, both in hardware and electricity bills, using open-source applications and utilities from the HPC community and our own software.

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