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+# HPC dictionary
+
+This dictionary focuses on *concepts*, *terminology*, and *technology* relevant
+to CSC's HPC environment. For a list of commonly used acronyms,
+[see the glossary](../glossary.md).
+
+---
+
+## 1. Core HPC Concepts
+
+### High‑Performance Computing (HPC)
+
+HPC refers to using supercomputers built from many interconnected compute nodes
+to run large or complex computations much faster than a normal workstation. CSC
+provides HPC systems such as Puhti, Mahti and LUMI for simulations, modelling,
+data analysis, and AI.
+
+### Supercomputer
+
+A supercomputer is a collection of high‑performance compute nodes connected
+with a fast interconnect and shared filesystems. CSC supercomputers provide
+advanced computing power, memory, and storage to handle intensive scientific
+workloads.
+
+### Parallel Computing
+
+Parallel computing breaks a problem into smaller tasks processed simultaneously
+on multiple CPU cores or GPUs. HPC systems use both shared‑memory (OpenMP)
+and distributed‑memory (MPI) parallelism to achieve scaling.
+
+### Compute Node
+
+A compute node is an individual server inside a supercomputer. Nodes include
+multiple CPU cores, memory, and sometimes GPUs or NVMe local storage. Different
+node types are optimized for different workloads.
+
+### CPU Computing
+
+CPU computing relies on general‑purpose processors suitable for a broad range
+of simulations, analyses, and numerical tasks. Systems like Mahti provide many
+high‑core‑count CPU nodes for parallel computation.
+
+### GPU Computing
+
+GPU computing uses massively parallel accelerators ideal for deep learning,
+data analytics, and certain scientific simulations. CSC systems offer Nvidia
+GPUs in Puhti and Mahti, while LUMI uses AMD MI250X GPUs.
+
+### Accelerated Computing
+
+Accelerated computing refers to using specialized hardware (e.g., GPUs) to
+speed up workloads that benefit from parallelism. LUMI‑G is specifically
+optimized for GPU‑accelerated workloads at very large scales.
+
+---
+
+## 2. Parallel Programming Concepts
+
+### Distributed‑Memory Parallelism (MPI)
+
+MPI enables programs to run across multiple nodes by exchanging messages
+between processes. It is essential for large multi-node simulations.
+
+### Shared‑Memory Parallelism (OpenMP)
+
+Shared‑memory parallelism uses threads within a single node to parallelize
+work. It is well suited for moderate parallelism that does not require multiple
+nodes.
+
+### Hybrid Parallelization
+
+Combines MPI between nodes and OpenMP threads within each node. This is often
+the best way to use multi‑core nodes efficiently.
+
+### Embarrassingly Parallel Workloads
+
+Workloads where tasks do not depend on one another and can run independently.
+These run efficiently on HPC systems using job arrays or other task
+farming-type execution.
+
+### Scalability
+
+The ability of a workload to benefit from additional compute resources. Unless
+the problem is embarrasingly parallel, scalability may often be limited by one
+or more factors such as communication bottlenecks, fraction of serial code or
+load imbalance. This is often the case with real HPC programs.
+
+---
+
+## 3. HPC Job Execution Concepts
+
+### Batch Job
+
+A non‑interactive job submitted to the scheduler using a Slurm script. It
+specifies resources (cores, memory, time, etc.) and runs automatically when
+resources become available.
+
+### Interactive Session
+
+A session on a compute node that allows immediate execution of commands. Useful
+for development, debugging, data exploration, or using notebooks, but not for
+large-scale simulations. Batch jobs are better for these.
+
+### Job Queue
+
+A waiting system where jobs are queued until required resources become
+available. The scheduler prioritizes jobs based on fair use policies,
+reservations, and resource demand.
+
+### Slurm Scheduler
+
+Slurm is the workload manager used on all CSC HPC systems. It handles job
+submission, queuing, resource allocation, and accounting.
+
+### Job Array
+
+A Slurm feature allowing the submission of many similar jobs simultaneously.
+Ideal for embarrassingly parallel workloads, but should not be used for a huge
+number of jobs, especially if they are short. If you need to run hundreds of
+short jobs, consider packaging them within a single job to reduce the load on
+the scheduler.
+
+### Resource Allocation
+
+Requesting CPU cores, GPUs, memory, storage, runtime, etc. when submitting a
+job. Efficient resource allocation improves queueing efficiency.
+
+---
+
+## 4. Storage & Data Concepts
+
+### Allas Object Storage
+
+A general‑purpose cloud‑based research data storage service accessible from
+CSC HPC systems and externally. Ideal for large datasets and data sharing.
+
+### Scratch Storage
+
+Temporary high‑performance filesystem for running jobs. Ideal for large
+intermediate files, but not intended for long‑term storage.
+
+### Local NVMe Storage
+
+Some nodes (e.g., certain Puhti and Mahti nodes) contain extremely fast local
+SSD storage. Great for I/O‑heavy workflows such as genomics, quantum chemistry,
+machine learning or data pre-/post-processing.
+
+### High-Speed Interconnect
+
+A low‑latency network connecting compute nodes in a cluster, crucial for MPI
+performance. CSC supercomputers use advanced fabrics to support large‑scale
+parallel workloads.
+
+---
+
+## 5. Software & Environment Concepts
+
+### Environment Modules
+
+Modules configure compilers, libraries, and software environments. They make it
+easy to switch between different versions of software stacks on CSC systems.
+
+### HPC Libraries
+
+Optimized math and communication libraries (e.g., BLAS, FFTW, ScaLAPACK)
+provide high‑performance numerical operations. CSC supplies several optimized
+versions adapted for CSC supercomputers.
+
+### Compiler Toolchains
+
+CSC offers multiple compiler suites (e.g., GCC, Intel OneAPI). Toolchains
+influence performance and must be compatible with linked libraries.
+
+### Containers in HPC
+
+Containers provide isolated software environments for complex workflows. CSC
+supports containers through Singularity/Apptainer on the HPC systems.
+
+### Python in HPC
+
+CSC provides optimized Python environments and guidance for effective Python
+usage on HPC, including virtual environments, HPC‑friendly libraries and
+container wrapper tools (Tykky).
+
+---
+
+## 6. Performance Concepts
+
+### Profiling
+
+Profiling examines how a program uses CPU time, memory, or I/O. Helps to
+identify performance bottlenecks and optimize HPC code for better performance.
+
+### Vectorization
+
+A technique leveraging CPU vector instructions to perform multiple operations
+at once. Modern CPUs like Xeon and EPYC use AVX to accelerate scientific
+workloads.
+
+### Load Balancing
+
+Ensuring equal distribution of computational work across cores or nodes to
+maximize performance. Critical for MPI workloads.
+
+### Memory Bandwidth
+
+The rate at which memory can feed data to the CPU/GPU. Many HPC workloads are
+memory‑bound rather than CPU‑bounded.
+
+---
+
+## 7. CSC‑Specific Concepts
+
+### CSC Project
+
+A project is required to access CSC computing, storage, and cloud resources.
+Projects define resource quotas, user membership, and billing units.
+
+### Billing Units (BUs)
+
+A unit used to measure the computing or storage resource consumption of a CSC
+project. CSC BUs come in four different flavors: CPU BU, GPU BU, Cloud BU and
+Storage BU.
+
+### Puhti
+
+A versatile supercomputer with many pre-installed applications, GPU capacity,
+and NVMe-enabled nodes. Recommended for new users and general workloads.
+
+### Mahti
+
+A massively parallel supercomputer optimized for large-scale CPU simulations.
+Suitable for scalable MPI applications. Mahti also has some GPU capacity and NVMe-enabled nodes, but less than Puhti and LUMI.
+
+### LUMI
+
+One of the world's fastest supercomputers, optimized for GPU‑accelerated
+workloads and advanced simulations at extreme scale. Located in CSC's Kajaani
+datacenter.
+
+### Roihu (coming in spring 2026)
+
+Roihu will replace Puhti and Mahti, providing next‑generation HPC
+capabilities with significantly increased performance.
+
+### Open OnDemand (OOD)
+
+A web‑based interface for accessing CSC supercomputers. Provides file
+browsing, job submission, terminal access, and interactive applications
+including graphical user interfaces.
+
+---
+
+## 8. Workflow & Research Computing Concepts
+
+### Simulation Workflow
+
+A typical HPC simulation workflow consists of pre‑processing (data
+preparation), computation (running jobs), and post‑processing
+(analysis/visualization).
+
+### Machine Learning in HPC
+
+HPC supports ML by providing large GPU partitions, scalable batch training, and
+fast local storage for datasets. LUMI‑G is specifically optimized for
+GPU‑based ML workloads.
+
+### Data Analytics in HPC
+
+Large datasets can be processed efficiently using parallel computing and GPU
+acceleration on CSC supercomputers, and stored in Allas object storage service.
+
+### Cloud + HPC Hybrid Workflows
+
+CSC's Pouta cloud allows customized environments, while HPC systems execute
+heavy computation. Cloud and HPC resources can be combined to enable advanced
+research use cases.
+
+---
diff --git a/docs/support/faq/index.md b/docs/support/faq/index.md
index 6a3e605014..076f1b850e 100644
--- a/docs/support/faq/index.md
+++ b/docs/support/faq/index.md
@@ -10,6 +10,7 @@
 * [Disk quota exceeded](disk-quota-exceeded.md)
 * [I need a Database, where can I deploy it?](database.md)
 * [Is there a glossary of the acronyms you keep using?](is-there-a-glossary-of-the-acronyms-you-keep-using.md)
+* [Is there a dictionary explaining CSC-related terminology and concepts?](csc-dictionary.md)
 * [How to write good support requests to CSC Service Desk](../support-howto.md)
 * [My SSH keys do not work](ssh-keys-not-working.md)
 * [I have questions about the Billing Unit renewal (2025)](bu-renewal.md)