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--- skill-tree:k:1:2:b [2020/07/19 11:30] – external edit 127.0.0.1
+++ skill-tree:k:1:2:b [2025/03/10 19:24] (current) – external edit 127.0.0.1
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-# K1.2-B Hardware Architectures
+# K1.2 Overview Hardware Architectures
-# Background
 HPC computer architectures are parallel computer architectures. A parallel computer is built out of
-  * Compute units,
+  * Compute units.
-  * Main memory, and
+  * Main memory.
-  * A high speed network.
+  * A high-speed network.
-# Aim
+## Learning Outcomes
-  * To provide knowledge about parallel computer architectures, in particular: the distinction between shared and distributed memory systems.
+  * elementary processing elements like CPUs, GPUs, many-core architectures
+  * vector systems, and FPGAs
+  * the NUMA architecture used for symmetric multiprocessing systems where the memory access time depends on the memory location relative to the processor
+  * network demands for HPC systems (e.g. high bandwidth and low latency)
+  * typical network architectures used for HPC systems, like fast Ethernet (1 or 10 Gbit) or InfiniBand
-# Outcomes
+  * Comprehend that in traditional **CPUs** - although CPU stands for Central Processing Unit - there is no central, i.e. single, processing unit any more because today all CPUs have multiple compute cores which all have the same functionality
-  *  elementary processing elements like CPUs, GPUs, many core architectures
+  * Comprehend that **GPUs** (Graphical Processing Units) or **GPGPUs** (General Purpose Graphical Processing Units) were originally used for image processing and displaying images on screens before people started to utilize the computing power of GPUs for other purposes
-  *  vector systems, and FPGAs
-  *  the NUMA architecture used for symmetric multiprocessing systems where the memory access time depends on the memory location relative to the processor
-  *  network demands for HPC systems (e.g. high bandwidth and low latency)
-  *  typical network architectures used for HPC systems, like fast Ethernet (1 or 10 Gbit) or InfiniBand
-  *  Comprehend that in traditional **CPUs** - although CPU stands for Central Processing Unit - there is no central, i.e. single, processing unit any more, because today all CPUs have multiple compute cores which all have the same functionality
-  * Comprehend that **GPUs** (Graphical Processing Units) or **GPGPUs** (General Purpose Graphical Processing Units) were originally used for image processing and displaying images on screens before people started to utilize the compute power of GPUs for other purposes
   * Comprehend that **FPGAs** (Field-Programmable Gate Arrays) are devices that have configurable hardware and configurations are specified by hardware description languages
   * Comprehend that **FPGAs** are interesting if one uses them to implement hardware features that are not available in CPUs or GPUs (e.g. low precision arithmetic that needs only a few bits)
-  * Comprehend that **Vector units** are successors of vector computers (i.e. the first generation of supercomputers) anf that they are supposed to provide higher memory bandwidth than CPUs
+  * Comprehend that **Vector units** are successors of vector computers (i.e. the first generation of supercomputers) and that they are supposed to provide higher memory bandwidth than CPUs
-  * Comprehend that at an abstract level the high speed network connects compute units and main memory which leads to three main parallel computer architectures
+  * Comprehend that at an abstract level the high-speed network connects compute units and main memory which leads to three main parallel computer architectures
     * **Shared Memory** where all compute units can directly access the whole main memory
     * **Distributed memory** where individual computers are connected with a network
     * **NUMA** (Non-Uniform Memory Access) combines properties from shared and distributed memory systems, because at the hardware level a NUMA system resembles a distributed memory
   * Comprehend that in general, the effort for programming parallel applications for distributed systems is higher than for shared memory systems
+  * parallelization techniques at the instruction level of a processing element (e.g. pipelining, SIMD processing)
+  * advanced instruction sets that improve parallelization (e.g., AVX-512)
+  * hybrid approaches, e.g. combining CPUs with GPUs or FPGAs
+  * typical network topologies and architectures used for HPC systems, like fat trees based on switched fabrics using e.g. fast Ethernet (1 or 10 Gbit) or InfiniBand
+  * special or application-specific hardware (e.g. TPUs)
+## Subskills
-# Subskills
-  * [[skill-tree:k:1:2:i]]
-  * [[skill-tree:k:1:2:e]]