# Parallel Simulations of the Sharp Wave-Ripples of the Hippocamp on Multicore CPUs and GPUs
## 3 Experimental Results
### Hardware Features of the Test Systems  
The serial and the multicore versions of the code have been validated and tested considering two different processors, namely an Intel Xeon Silver 4314 and an Intel i9-13900K. These processors have been designed to meet a variety of computing requirements, and each offers a distinctive set of hardware features. The Xeon Silver 4314 is designed for use in enterprise-grade servers and data centres, where reliability, scalability, and multi-threaded performance are of critical importance. The processor features multiple cores and threads that are optimized for parallel processing, extensive cache memory, and support for error-correcting code (ECC) memory, which enhances data integrity in mission-critical environments. Additionally, the Xeon Silver 4314 is compatible with advanced virtualisation features, making it an appropriate choice for tasks such as virtualisation, database management, and large-scale computing. Conversely, the Intel Core i9-13900K is a high-performance desktop processor designed for programmers, gamers, and content creators. The processor in question boasts higher base and turbo clock speeds, which are designed to deliver exceptional single-threaded performance and responsiveness in gaming and creative applications. Additionally, the i9-13900K incorporates Intel's hybrid architecture, comprising a combination of Performance-cores (P-cores) and Efficient-cores (E-cores), which enables it to manage a range of tasks with optimal efficiency, from demanding gaming to background operations. The main features of these two processors are summarized in Table 2.  
Concerning the CUDA version of the hippocampus CA1 network simulation, two GPUs have been considered: NVIDIA A16 and NVIDIA RTX 4090 (NVIDIA, Santa Clara, CA, USA). These GPUs are both powerful, yet they are designed for markedly disparate use cases, offering distinctive hardware features to meet particular needs. The NVIDIA A16 GPU features a multi-GPU architecture, whereby a single card incorporates four discrete GPUs, each comprising its own set of CUDA cores, memory, and other processing units. This configuration enables the A16 to effectively manage multiple concurrent virtual desktops or workstations on a single card, offering up to 64 users per card with dedicated GPU resources. The independence of the four GPUs guarantees that workloads are isolated and can be separately managed, making the A16 an optimal choice for high-density virtual desktop infrastructure (VDI) environments where scalability and resource allocation are paramount. The device is constructed on the NVIDIA Ampere architectural framework and is intended to facilitate the provision of high-density, high-performance virtual desktops and workstations, with the capacity to support up to 64 concurrent users per card. The A16 is particularly well-suited to scenarios where multiple users require efficient sharing of GPU resources, making it an optimal choice for large-scale deployment in data centres. In contrast, the NVIDIA RTX 4090 is a consumer-oriented GPU aimed at gamers and content creators who demand the highest levels of graphical performance. As one of the most powerful GPUs currently available, the RTX 4090 is based on NVIDIA's Ada Lovelace architecture, delivering exceptional performance in gaming, 3D rendering, AI processing, and other graphics-intensive tasks. It features a high number of CUDA cores, significant VRAM, and support for advanced ray tracing and AI features, making it the optimal choice for users seeking the best possible performance in desktop graphics. Table 3 compares the features of the two adopted GPUs.