1.1 - HPC landscape overview

September 2025

Section 1.1: HPC landscape overview

Key Concept

High-Performance Computing (HPC) includes various strategies to accelerate computation by leveraging hardware parallelism. Choosing the right approach depends on the nature of the problem, the scale of data, and available infrastructure.

High-Performance Computing (HPC) is no longer a niche field; it's a critical enabler for scientific discovery, engineering innovation, and advanced analytics. HPC aims to solve problems that are too complex or time-consuming for standard computing systems. This requires leveraging powerful computational resources and sophisticated techniques. The HPC landscape is diverse, encompassing a range of hardware and software approaches, each with its strengths and weaknesses. Understanding these options is crucial for choosing the right approach for a given problem. This section provides an overview of the major techniques used in HPC.

When people think of HPC, they often imagine huge supercomputers — but HPC can also emerge from distributed communities. Projects like Folding@Home or SETI@Home coordinate millions of volunteer CPUs worldwide, achieving exascale performance through collaboration rather than a single monolithic system.

Topics

  • Multicore CPUs: Ideal for general-purpose, moderately parallel tasks using shared memory.
    • GPUs: Well-suited for highly parallel workloads like matrix operations or simulations.
      • Clusters (Distributed Computing): Used for large-scale problems that require many machines, often with message passing.
        • Specialized Hardware (FPGAs/ASICs): Offer peak performance for specific applications but require more development effort.

          Multicore CPUs:

          At the foundation of most modern HPC systems are multicore CPUs. These processors contain multiple independent processing units (cores) on a single chip. This allows for true parallel execution, where different cores can work on different parts of a problem simultaneously. Multicore CPUs are well-suited for problems that can be easily divided into smaller, independent tasks. They are often used in applications like database management, web servers, and some scientific simulations. The programming model for multicore CPUs often involves threading, where the application is divided into threads that are executed concurrently on different cores. While relatively easy to implement, scaling beyond a certain number of cores can be challenging due to factors like memory bandwidth limitations and communication overhead.

          GPUs:

          Graphics Processing Units (GPUs) initially developed for accelerating graphics rendering, have emerged as powerful accelerators for a wide range of HPC applications. GPUs are massively parallel processors, containing thousands of smaller cores optimized for performing the same operation on multiple data points simultaneously. This makes them exceptionally well-suited for computationally intensive tasks like deep learning, image processing, and scientific simulations involving large datasets. GPUs excel at data-parallel problems, where the same operation needs to be applied to many data elements. Programming GPUs typically involves languages like CUDA (NVIDIA) or OpenCL, which require a different programming paradigm than traditional CPU programming. While GPUs offer significant performance gains, they are not a universal solution and may not be the best choice for all HPC applications.

          Clusters (Distributed Computing):

          Clusters are groups of interconnected computers that work together as a single system. Each computer in the cluster (a node) has its own CPU, memory, and storage. Clusters are ideal for tackling problems that can be divided into smaller tasks that can be distributed across multiple machines. This approach allows for significant scalability, as the computational power of the cluster can be increased by adding more nodes. Clusters are commonly used for scientific simulations, weather forecasting, and data analytics. The software used to manage and coordinate tasks across a cluster is often based on distributed computing frameworks like Slurm, PBS, or Kubernetes. Communication between nodes in a cluster is typically achieved using message passing techniques.

          Specialized Hardware (FPGAs/ASICs):

          Field-Programmable Gate Arrays (FPGAs) and Application-Specific Integrated Circuits (ASICs) represent the most specialized and often the highest-performing HPC solutions. FPGAs are programmable hardware devices that can be configured to implement custom logic circuits. This allows for highly optimized solutions for specific applications. ASICs, on the other hand, are custom-designed chips that are tailored to a particular task. While FPGAs and ASICs offer the greatest potential for performance gains, they are also the most expensive and require significant expertise to develop and deploy. They are typically used in applications where performance is paramount, such as financial modeling, cryptography, and certain scientific simulations.

          Note: The following subsections were not planned but were generated by the model during expansion. They reflect related HPC topics that go beyond the scope of the original outline. We keep them here too, as an example of LLM verbosity rampage.

          Parallelism and Scalability: The Core Principles

          The ability to effectively utilize HPC resources hinges on understanding the principles of parallelism and scalability. These concepts dictate how a problem is broken down and distributed across computational resources.

          Data Parallelism:

          Data parallelism involves applying the same operation to different parts of a dataset simultaneously. This is a common approach for problems where the same calculations need to be performed on a large number of data elements. GPUs are particularly well-suited for data-parallel problems due to their massively parallel architecture. Examples include image processing, video encoding, and machine learning training. The key is to ensure that the data is properly partitioned and that the operations are efficiently executed across all processing units.

          Task Parallelism:

          Task parallelism involves breaking down a problem into a set of independent tasks that can be executed concurrently. Each task can be assigned to a different processor or core. This approach is suitable for problems where the tasks are not inherently data-parallel. For example, a simulation might involve calculating the trajectory of multiple particles, where each particle's trajectory can be calculated independently. Clusters are often used for task-parallel problems, as they allow for the distribution of tasks across multiple machines.

          Scalability:

          Scalability refers to the ability of a system to handle increasing workloads by adding more resources. A scalable system can maintain its performance as the number of processors or nodes increases. Scalability is a critical consideration in HPC, as it allows for the ability to tackle increasingly complex problems. However, achieving scalability is not always straightforward. Factors like communication overhead, load balancing, and data distribution can limit scalability. Careful design and optimization are required to ensure that a system can scale effectively.

          Software and Programming Models: Tools of the Trade

          To harness the power of HPC systems, developers rely on a variety of software tools and programming models. These tools provide the necessary abstractions and libraries to manage parallelism, communication, and data distribution.

          Message Passing Interface (MPI):

          MPI is a widely used standard for message passing, enabling communication between processes running on different nodes in a cluster. It provides a set of functions for sending and receiving data, coordinating tasks, and synchronizing processes. MPI is a fundamental tool for distributed computing and is commonly used in scientific simulations, weather forecasting, and data analytics. While powerful, MPI can be complex to program with, requiring careful attention to communication patterns and data distribution.

          CUDA:

          CUDA (Compute Unified Device Architecture) is a parallel computing platform and programming model developed by NVIDIA. It allows developers to leverage the power of GPUs for general-purpose computing. CUDA provides a set of libraries and tools for writing programs that run on GPUs, enabling significant performance gains for computationally intensive tasks. CUDA is widely used in deep learning, image processing, and scientific simulations.

          OpenMP:

          OpenMP is a standard for shared-memory parallel programming. It provides a set of directives and runtime library calls that allow developers to easily parallelize their code on multicore CPUs. OpenMP is relatively easy to use and can be applied to existing code with minimal modifications. It is commonly used in applications like scientific simulations, image processing, and database management.

          Dask:

          Dask is a flexible parallel computing library for Python. It provides a high-level API for parallelizing data-intensive tasks. Dask can be used to parallelize tasks on a single machine or across a cluster. It is particularly well-suited for working with large datasets that do not fit into memory. Dask integrates well with other Python libraries like NumPy, Pandas, and Scikit-learn.

          Exercise

          For each of the techniques, name one problem or domain where it shines (e.g., GPUs for image processing, MPI clusters for weather simulation).

          • Multicore CPUs → Data compression, e.g., running multiple threads to compress files in parallel using shared memory.
            • GPUs → Image recognition, e.g., treating each image as an n × m pixel matrix and performing convolution operations across many images simultaneously.
              • Clusters (Distributed Computing) → Weather forecasting, e.g., splitting the atmosphere into grid cells and simulating physical processes with MPI across thousands of nodes.
                • Specialized Hardware (FPGAs/ASICs) → High-frequency trading, e.g., implementing decision logic directly in hardware to minimize latency to the microsecond scale.

                  Common Pitfall

                  Overestimating GPU benefit without accounting for data transfer and memory constraints.

                  Best Practice

                  Benchmark different approaches early to identify the most scalable solution for your task.