High Performance Computing Service Research Articles

AI-Based Approach to Firewall Rule Refinement on High-Performance Computing Service Network

High-performance computing (HPC) relies heavily on network security, particularly when supercomputing services are provided via public networks. As supercomputer operators, we introduced several security devices, such as anti-DDoS, intrusion prevention systems (IPSs), firewalls, and web application firewalls, to ensure the secure use of supercomputing resources. Potential threats are identified based on predefined security policies and added to the firewall rules for access control after detecting abnormal behavior through anti-DDoS, IPS, and system access logs. After analyzing the status change patterns for rule policies added owing to human errors among these added firewall log events, 289,320 data points were extracted over a period of four years. Security experts and operators must go through a strict verification process to rectify policies that were added incorrectly owing to human error, which adds to their workload. To address this challenge, our research applies various machine- and deep-learning algorithms to autonomously determine the normalcy of detection without requiring administrative intervention. Machine-learning algorithms, including naïve Bayes, K-nearest neighbor (KNN), OneR, a decision tree called J48, support vector machine (SVM), logistic regression, and the implemented neural network (NN) model with the cross-entropy loss function, were tested. The results indicate that the KNN and NN models exhibited an accuracy of 97%. Additional training and feature refinement led to even better improvements, increasing the accuracy to 98%, a 1% increase. By leveraging the capabilities of machine-learning and deep-learning technologies, we have provided the basis for a more robust, efficient, and autonomous network security infrastructure for supercomputing services.

UrsaX: Integrating Block I/O and Message Transfer for Ultrafast Block Storage on Supercomputers

It is increasingly important for the next-generation exascale supercomputers to extend its applications beyond traditional HPC (high-performance computing) scenarios, so as to achieve high social and economic benefit. Similar to AWS (Amazon Web Services) and Alibaba Cloud, cloud-style virtual HPC service is a promising application scenario on supercomputers, for which remote block storage is the key to provide tenants with supercomputers extremely-high storage performance. Unfortunately, the state-of-the-art block storage software systems (such as URSA and Ceph) cannot adapt to the advanced hardware features of supercomputers. This paper presents UrsaX, an efficient block storage service for our next-generation Tianhe exascale supercomputer that is equipped with the high-performance GLEX network and NVMe (Non-Volatile Memory Express) SSDs. UrsaXs virtual disks, which can be mounted like normal physical ones, enable not only traditional HPC applications but also supercomputeroblivious POSIX applications to enjoy the high performance of supercomputers. At the core of UrsaX is with a novel design of the efficient integration of on-disk block I/O and in-network message transfer on supercomputers. UrsaX utilizes the NVMe Fabrics kernel module to expand the NVMe standard on the supercomputer network, and separates metadata I/O and data I/O of blocks respectively being handled over the MP (Mini Packet) and RDMA (Remote Direct Memory Access) protocols. We thoroughly explore the design space for remote block storage on supercomputers including parallelism, scalability, fault tolerance, and consistency. We conduct extensive evaluation on a subset of our exascale supercomputer consisting of 44 storage machines (each with four NVMe SSDs). The result shows that UrsaX achieves local-storage-level I/O latency (tens of microseconds) while being able to linearly increase the aggregate performance (IOPS and throughput) as the system scale increases, an order of magnitude higher than the state-of-the-art block storage systems.

Value co-creation in a high-performance computing (HPC) service ecosystem: opportunities for European SMEs

PurposeThis paper aims to develop a conceptual framework as a valuable investigative tool for future empirical studies examining stakeholders’ interactions within a high-performance computing (HPC) service ecosystem and identifying the enablers for better value co-creation under various service provisioning contexts.Design/methodology/approachThis conceptual paper follows a theory adaptation research design that systematically examines the literature on a substantive topic encompassing HPC technology adoption and usage by European small and medium-sized enterprises (SMEs). Furthermore, it introduces theoretical perspectives from service innovation research as its methodological approach (method theory) to develop a theoretical framework for value co-creation within an HPC service ecosystem.FindingsThe conceptual framework presented in this paper identifies the critical resource elements of SMEs, which can potentially be integrated with other stakeholders’ resources and complement the limited internal resources of SMEs for enhanced value co-creation within an HPC service ecosystem.Research limitations/implicationsThe conceptualisation of an HPC service ecosystem and the potential of value co-creation through integrating stakeholders’ resources, presented in this paper, underscore further empirical research in this direction.Practical implicationsFindings from this paper can inform European policymakers for regional development to frame regional strategies and policies that enable SMEs to better uptake HPC services. Service providers can revisit their business models to reconfigure their organisational resources for enhanced value proposition and co-creation.Social implicationsThis study advances the agenda of transformative service research that aims uplifting changes and societal well-being through a more inclusive and sustainable value co-creation among a wider set of stakeholders within the service ecosystem.Originality/valueThe paper addresses an under-investigated research gap by examining SMEs’ adoption of HPC services and opportunities for higher value co-creation using a service ecosystem approach based on multi-actor interaction, collaboration and resource integration.

Namibia's first high performance computer

High performance computing (HPC) refers to the practice of aggregating computing power of several computing nodes in a way that delivers much higher performance than one could achieve by a typical desktop computer in order to solve large problems in business, science, or engineering. The University of Namibia has so far received two HPC racks from the Centre for High Performance Computing in South Africa, of which one is operational. The primary use of the rack was foreseen to be human capacity development and awareness in HPC and to form part of Namibia’s readiness in participating in the Square Kilometre Array (SKA) and the African Very Long Baseline Interferometry Network (AVN) projects, but is now also being used for research in multi-wavelength astronomy and beyond. This is one of the first HPC services set up and operated by an entirely African team. We perform tests to benchmark the computational power and data transfer capabilities of the system and find that each node, on average, has a peak performance power of 82.4±1.1 GFLOPS. We also summarise all the projects that have enlisted the HPC facility.

AFFORDABLE COMPUTATION FOR ARCHITECTURE

Current architectural requirements prioritize the need to minimize the ecological footprint. By taking advantage of computational design approaches like Algorithmic Design (AD), architects can enhance their design processes with analysis, optimization, and visualization mechanisms, which are critical to explore design solutions that meet this need. However, these mechanisms are also highly time- and resource-consuming, often implying a quality tradeoff or the acquisition of High-Performance Computing (HPC) machines. The latter are not yet affordable for most design studios but, fortunately, they can be contracted as a service. This paper evaluates the impact of computation as a service in architecture and, more specifically, the remote use of HPC for AD, with the aim of reducing the time and costs associated with computationally expensive processes. A set of experiments were made involving analysis, optimization, and rendering of a selected case study. Results indicate that HPC services are advantageous, particularly when performing embarrassingly parallelizable tasks such as rendering. However, some challenges remain, namely the required expertise.

Ubiquitous Power Internet of Things-Oriented Low-Latency Edge Task Scheduling Optimization Strategy

Internet of things of cloud computing offers high-performance computing, storage and networking services, but there are still suffers from a high transmission and processing latency, poor scalability and other problems. Internet of things of edge computing can better meet the increasing requirements of electricity consumers for service quality, especially the increasingly stringent need for low delay. On the other hand, edge intelligent network technology can offers edge smart sensing while significantly improve the efficiency of task execution, but it will lead to a massive collaborative task scheduling optimization problem. In order to solve this problem, This paper studies an ubiquitous power internet of things (UPIoT) smart sensing network edge computing model and an improved multi node cluster cooperative scheduling optimization strategy. The cluster server is added to the edge aware computing network, and an improved low delay edge task collaborative scheduling algorithm (LLETCS) is designed by using the vertical cooperation and multi node cluster collaborative computing scheme between edge aware networks. Then the problem is transformed based on linear reconstruction technology, and a parallel optimization framework for solving the problem is proposed. The simulation results suggest that the proposed scheme can more effectively reduce the UPIoT edge computing latency, and improve the quality of service in UPIoT smart sensing networks.

SAGMAD—A Signature Agnostic Malware Detection System Based on Binary Visualisation and Fuzzy Sets

Image conversion of byte-level data, or binary visualisation, is a relevant approach to security applications interested in malicious activity detection. However, in practice, binary visualisation has always been seen to have great limitations when dealing with large volumes of data, and would be a reluctant candidate as the core building block of an intrusion detection system (IDS). This is due to the requirements of computational time when processing the flow of byte data into image format. Machine intelligence solutions based on colour tone variations that are intended for pattern recognition would overtax the process. In this paper, we aim to solve this issue by proposing a fast binary visualisation method that uses Fuzzy Set theory and the H-indexing space filling curve. Our model can assign different colour tones on a byte, allowing it to be influenced by neighbouring byte values while preserving optimal locality indexing. With this work, we wish to establish the first steps in pursuit of a signature-free IDS. For our experiment, we used 5000 malicious and benign files of different sizes. Our methodology was tested on various platforms, including GRNET’s High-Performance Computing services. Further improvements in computation time allowed larger files to convert in roughly 0.5 s on a desktop environment. Its performance was also compared with existing machine learning-based detection applications that used traditional binary visualisation. Despite lack of optimal tuning, SAGMAD was able to achieve 91.94% accuracy, 90.63% precision, 92.7% recall, and an F-score of 91.61% on average when tested within previous binary visualisation applications and following their parameterisation scheme. The results exceeded malware file-based experiments and were similar to network intrusion applications. Overall, the results demonstrated here prove our method to be a promising mechanism for a fast AI-based signature-agnostic IDS.

Basics of Bash.

Use of the Bash command shell and language is one of the fundamental skills of a bioinformatician. This language is required for accessing high performance computing (HPC) services and effectively using these resources to improve your analyses. Bash is completely text based, which is different from many graphic based operating systems, but this language is also highly powerful, allowing for significant automation and reproducibility within analysis pipelines. This chapter aims to teach the fundamentals of Bash, including how to create files and folders, how to sort and search through files, and how to use pipes and loops to automate processes. By the end of this chapter, readers should be ready to undertake their first simple bioinformatics analysis.

Modeling, Verification, and Signal Integrity Analysis of High-Speed Signaling Channel with Tabbed Routing in High Performance Computing Server Board

It is necessary to reduce the crosstalk noise in high-speed signaling channels. In the channel routing area, the tabbed routing pattern is used to mitigate far-end crosstalk (FEXT), and the electrical length is controlled with a time domain reflectometer (TDR) and time domain transmission (TDT). However, unlike traditional channels having uniform width and space, the width and space of tabbed routing changes by segment, and the capacitance and inductance values of tabbed routing also change. In this paper, we propose a tabbed routing equivalent circuit modeling method using the segmentation approach. The proposed model was verified using 3D EM simulation and measurement results in the frequency domain. Based on the calculated inductance and capacitance parameters, we analyzed the insertion loss, FEXT, and self-impedance in the frequency domain, and TDT and FEXT in the time domain, by comparing the values of these metrics with and without tabbed routing. Using the proposed tabbed routing model, we analyzed tabbed routing with variations of design parameters based on self- and mutual-capacitance and inductance.

Molecular dynamics simulation of the interactions between carbon dioxide and a natural-based carbonaceous microporous material

Oil palm plantation has drastically changed the scenario of Malaysian agriculture and economy since the second half of the 20th century. World palm oil production in 1980 was around 5.0 million tonnes, doubled to 11.0 million tonnes in 1990, reaching at the beginning of the 21th century approximatively 21.8 million tonnes per year. Malaysia is responsible of about half of the world palm oil production (10.8 million tonnes), thus becoming the world’s largest producer and exporter of palm oil. Oil palm industries also generate large amounts of lignocellulosic biomass waste as a sub-product, which currently has no economic market value other than feedstock for energy valorisation in some limited cases. The utilization and reformulation of this biomass as new materials with added value, would reduce accumulation of waste, mitigating the environmental impact of such intensive industry sector, and would simultaneously generate further economic revenue. In this work, we have studied the interactions of CO2/N2 streams with one slit-like graphite layers, using Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) molecular dynamics simulations, to simulate new carbon-based adsorbents from empty fruit bunches (EFB). Simulations were conducted using the high-performance computing (HPC) service provided by the University of Aberdeen (Scotland, UK), following accuracy tests to yield minimum operational costs. The simulations provided density profiles of CO2 and N2 molecules as a function of temperature (300/400/500 K), pressure (1/5/10 atm) and micro-meso porosity (12/20/30 A). The results showed that the number of CO2 molecules retained in the graphite framework decreases at higher temperatures and lower pressures, in accordance with the exothermicity of the process, and smaller pore sizes promote CO2 adsorption at the tested pressures, due to the superposition of the Van der Waals force given by two adjacent walls of slit pores. Our findings exhibit the potential of lignocellusic materials from palm oil tree as materials for CO2 capture.

Traffic and overhead analysis of applied pre-filtering ACL firewall on HPC service network

In an high-performance computing (HPC), supercomputing service environment, the security of infrastructure nodes that are points of contact for researchers is very important. We have applied various security devices such as anti-DDoS, IPS, firewall, web application firewall, and etc. on an HPC service network to provide more secure supercomputing services. Firewalls are a common and essential element of network security devices with the ability to block network traffic according to pre-defined rules. With the increasing demands for services, cyberattacks, as well as overheads on firewall policies have also increased. To reduce this overhead, in our previous research, we analyzed dropped packets log and performed a method on the firewall as Abnormal IP that can detect and deny anomalous IPs in real-time. As the number of abnormal IPs increased, the performance of the firewall significantly deteriorated. To solve this problem, we applied access control list (ACL) at the front-end of the firewall to perform pre-filtering, thereby improving the performance of the firewall on the HPC service network. This research is expected to contribute as a preliminary study in the HPC field by deriving pre-filtering ACL to reduce the CPU load of firewall server by showing the result of about 21.5% improvement in performance.

An Instance Reservation Framework for Cost Effective Services in Geo-Distributed Data Centers

Infrastructure-as-a-Service clouds in geo-distributed data centers offer various pricing options, including on-demand and reserved instances, which provide an elastic and cost-effective infrastructure to support High Performance Computing (HPC) applications. In this paper, we propose an instance reservation based cloud service framework, modeling the cost-minimizing reservation decision issue as an NP-hard integer programming problem for distributed data centers. To ease its computation complexity, two algorithms are proposed to minimize the HPC service cost with the worst-case performance guarantees: an offline heuristic-greedy algorithm, and a rolling-horizon based online algorithm when only short-term demand prediction is available. Facing fluctuating demands, instance reservation in a single data center may incur the highly underutilized capacity. To address this issue for further cost reduction, we extend the scheme with a novel cloud broker federation based resource sharing mechanism, reallocating already reserved but unused instances to computation-intensive and short-lived tasks for continuous execution without interruption. Extensive evaluations driven by large-scale trace-based datasets demonstrate that the proposed mechanism can effectively handle large volumes of service requests, saving considerable service costs with higher reservation resource utilization.

Open Source Framework for Enabling HPC and Cloud Geoprocessing Services

Geoprocessing is a set of tools that can be used to efficiently address several pressing chal-lenges for the global economy ranging from agricultural productivity, the design of transport networks, to the prediction of climate change and natural disasters. This paper describes an Open Source Framework developed, within three European projects, for Ena-bling High-Performance Computing (HPC) and Cloud geoprocessing services applied to agricultural challenges. The main goals of the European Union projects EUXDAT (EUro-pean e-infrastructure for eXtreme Data Analytics in sustainable developmenT), CYBELE (fostering precision agriculture and livestock farming through secure access to large-scale HPC-enabled virtual industrial experimentation environment empowering scalable big data analytics), and EOPEN (opEn interOperable Platform for unified access and analysis of Earth observatioN data) are to enable the use of large HPC systems, as well as big data management, user-friendly access and visualization of results. In addition, these projects focus on the development of software frameworks, and fuse Earth-observation data, such as Copernicus data, with non-Earth-observation data, such as weather, environmental and social media information. In this paper, we describe the agroclimatic-zones pilot used to validate the framework. Finally, performance metrics collected during the execution (up to 182 times speedup with 256 MPI processes) of the pilot are presented.

Gap-Free Monitoring of Annual Mangrove Forest Dynamics in Ca Mau Province, Vietnamese Mekong Delta, Using the Landsat-7-8 Archives and Post-Classification Temporal Optimization

Ecosystem services offered by mangrove forests are facing severe risks, particularly through land use change driven by human development. Remote sensing has become a primary instrument to monitor the land use dynamics surrounding mangrove ecosystems. Where studies formerly relied on bi-temporal assessments of change, the practical limitations concerning data-availability and processing power are slowly disappearing with the onset of high-performance computing (HPC) and cloud-computing services, such as in the Google Earth Engine (GEE). This paper combines the capabilities of GEE, including its entire Landsat-7 and Landsat-8 archives and state-of-the-art classification approaches, with a post-classification temporal analysis to optimize land use classification results into gap-free and consistent information. The results demonstrate its application and value to uncover the spatio-temporal dynamics of mangrove forests and land use changes in Ngoc Hien District, Ca Mau province, Vietnamese Mekong delta. The combination of repeated GEE classification output and post-classification optimization provides valid spatial classification (94–96% accuracy) and temporal interpolation (87–92% accuracy). The findings reveal that the net change of mangroves forests over the 2001–2019 period equals −0.01% annually. The annual gap-free maps enable spatial identification of hotspots of mangrove forest changes, including deforestation and degradation. Post-classification temporal optimization allows for an exploitation of temporal patterns to synthesize and enhance independent classifications towards more robust gap-free spatial maps that are temporally consistent with logical land use transitions. The study contributes to a growing body of work advocating full exploitation of temporal information in optimizing land cover classification and demonstrates its use for mangrove forest monitoring.

Analysis of Thermal Performance Metrics—Application to CPU Cooling in HPC Servers

Optimization of heatsinks has a critical importance for the energy-efficient cooling of high-performance computing (HPC) servers to meet reliability, performance, and cost targets. This article provides a detailed application of the effectiveness as a performance metric to provide a qualitative comparison of the CPU heatsink solutions. First, the use of effectiveness as a metric to provide an absolute performance of heatsinks on a simple scale of 0–1 is highlighted. Then, the effectiveness and thermal resistances are compared to explore the limits of air cooling and the relative merits of liquid cooling. Second, a response surface method (RSM) for the heatsink optimization is presented. Furthermore, a response surface optimization (RSO) based on the design of experiments (DOE) is performed on the numerical models of the heatsink and cold plates using FloTHERM computational fluid dynamics (CFD) software. The simulation results obtained at 45 °C inlet are used to provide an objective comparison among the optimum heatsink and cold plates. The simulations show that heatsinks have the effectiveness of 0.65–0.75 and a cooling capability of 150–250 W. The cold plates have an effectiveness of 0.25–0.30 and a cooling capability of 325–425 W. Furthermore, the simulation results are compared with benchmark validation studies. Counterintuitively, the higher effectiveness obtained for the air-cooled heatsinks indicates limited scope for further improvement, whereas the cold plates (with lower effectiveness) provide great scope for further improvements to meet future cooling challenges.

TeraChem Cloud: A High-Performance Computing Service for Scalable Distributed GPU-Accelerated Electronic Structure Calculations.

The encapsulation and commoditization of electronic structure arise naturally as interoperability, and the use of nontraditional compute resources (e.g., new hardware accelerators, cloud computing) remains important for the computational chemistry community. We present TeraChem Cloud, a high-performance computing service (HPCS) that offers on-demand electronic structure calculations on both traditional HPC clusters and cloud-based hardware. The framework is designed using off-the-shelf web technologies and containerization to be extremely scalable and portable. Within the HPCS model, users can quickly develop new methods and algorithms in an interactive environment on their laptop while allowing TeraChem Cloud to distribute ab initio calculations across all available resources. This approach greatly increases the accessibility of hardware accelerators such as graphics processing units (GPUs) and flexibility for the development of new methods as additional electronic structure packages are integrated into the framework as alternative backends. Cost-performance analysis indicates that traditional nodes are the most cost-effective long-term solution, but commercial cloud providers offer cutting-edge hardware with competitive rates for short-term large-scale calculations. We demonstrate the power of the TeraChem Cloud framework by carrying out several showcase calculations, including the generation of 300,000 density functional theory energy and gradient evaluations on medium-sized organic molecules and reproducing 300 fs of nonadiabatic dynamics on the B800-B850 antenna complex in LH2, with the latter demonstration using over 50 Tesla V100 GPUs in a commercial cloud environment in 8 h for approximately $1250.

A component-based framework for certification of components in a cloud of HPC services

• HPC Shelf is a component-oriented cloud computing platform for HPC Services. • A component-oriented certification framework has been proposed for HPC Shelf. • The certification framework integrates formal verification tools. • The certification framework supports the VaaS (Verification-as-a-Service) paradigm. • SWC2 is a certifier for scientific workflows in HPC Shelf. HPC Shelf is a proposal of a cloud computing platform to provide component-oriented services for High Performance Computing (HPC) applications. This paper presents a Verification-as-a-Service (VaaS) framework for component certification on HPC Shelf . Certification is aimed at providing higher confidence that components of parallel computing systems of HPC Shelf behave as expected according to one or more requirements expressed in their contracts. To this end, new abstractions are introduced, starting with certifier components. They are designed to inspect other components and verify them for different types of functional, non-functional and behavioral requirements. The certification framework is naturally based on parallel computing techniques to speed up verification tasks.

Migrating Engineering Windows HPC applications to Linux HTCondor and Slurm Clusters

The CERN IT department has been maintaining different High Performance Computing (HPC) services over the past five years. While the bulk of computing facilities at CERN are running under Linux, a Windows cluster was dedicated for engineering simulations and analysis related to accelerator technology development. The Windows cluster consisted of machines with powerful CPUs, big memory, and a low-latency interconnect. The Linux cluster resources are accessible through HTCondor, and are used for general purpose parallel but single-node type jobs, providing computing power to the CERN experiments and departments for tasks such as physics event reconstruction, data analysis, and simulation. For HPC workloads that require multi-node parallel environments for Message Passing Interface (MPI) based programs, there is another Linux-based HPC service that is comprised of several clusters running under the Slurm batch system, and consist of powerful hardware with low-latency interconnects.In 2018, it was decided to consolidate compute intensive jobs in Linux to make a better use of the existing resources. Moreover, this was also in line with CERN IT strategy to reduce its dependencies on Microsoft products. This paper focuses on the migration of Ansys [1], COMSOL [2] and CST [3] users from Windows HPC to Linux clusters. Ansys, COMSOL and CST are three engineering applications used at CERN for different domains, like multiphysics simulations and electromagnetic field problems. Users of these applications are in different departments, with different needs and levels of expertise. In most cases, the users have no prior knowledge of Linux. The paper will present the technical strategy to allow the engineering users to submit their simulations to the appropriate Linux cluster, depending on their simulation requirements. We also describe the technical solution to integrate their Windows workstations in order from them to be able to submit to Linux clusters. Finally, we discuss the challenges and lessons learnt during the migration.

Development of Operational Technology for Meteorological High Performance Computing

As an important branch of information technology, high-performance computing has expanded its application field and its influence has been expanding. High-performance computing is always a key area of application in meteorology. We used field research and literature review methods to study the application of high performance computing in China’s meteorological department, and obtained the following results: 1) China Meteorological Department gradually established the first high-performance computer system since 1978. High-performance computing services can support operational numerical weather prediction models. 2) The Chinese meteorological department has always used the relatively advanced high-performance computing technology, and the business system capability has been continuously improved. The computing power has become an important symbol of the level of meteorological modernization. 3) High-performance computing technology and meteorological numerical forecasting applications are increasingly integrated, and continue to innovate and develop. 4) In the future, high-performance computing resource management will gradually transit from the current local pre-allocation mode to the local remote unified scheduling and shared use. In summary, we have come to the conclusion that the performance calculation business of the meteorological department will usher in a better tomorrow.

Network slicing to improve multicasting in HPC clusters

In high performance computing (HPC) resources’ extensive experiments are frequently executed. HPC resources (e.g. computing machines and switches) should be able to handle running several experiments in parallel. Typically HPC utilizes parallelization in programs, processing and data. The underlying network is seen as the only non-parallelized HPC component (i.e. no dynamic virtual slicing based on HPC jobs). In this scope we present an approach in this paper to utilize software defined networking (SDN) to parallelize HPC clusters among the different running experiments. We propose to accomplish this through two major components: A passive module (network mapper/remapper) to select for each experiment as soon as it starts the least busy resources in the network, and an SDN-HPC active load balancer to perform more complex and intelligent operations. Active load balancer can logically divide the network based on experiments’ host files. The goal is to reduce traffic to unnecessary hosts or ports. An HPC experiment should multicast, rather than broadcast to only cluster nodes that are used by the experiment. We use virtual tenant network modules in Opendaylight controller to create VLANs based on HPC experiments. In each HPC host, virtual interfaces are created to isolate traffic from the different experiments. The traffic between the different physical hosts that belong to the same experiment can be distinguished based on the VLAN ID assigned to each experiment. We evaluate the new approach using several HPC public benchmarks. Results show a significant enhancement in experiments’ performance especially when HPC cluster experiences running several heavy load experiments simultaneously. Results show also that this multi-casting approach can significantly reduce casting overhead that is caused by using a single cast for all resources in the HPC cluster. In comparison with InfiniBand networks that offer interconnect services with low latency and high bandwidth, HPC services based on SDN can provide two distinguished objectives that may not be possible with InfiniBand: The first objective is the integration of HPC with Ethernet enterprise networks and hence expanding HPC usage to much wider domains. The second objective is the ability to enable users and their applications to customize HPC services with different QoS requirements that fit the different needs of those applications and optimize the usage of HPC clusters.