Common computing techniques might not be as efficient in problem-solving as modern computers. However, scientific computation serves as the foundation of present-day machine learning software. Currently, computers are used to perform two common types of mathematical computations:
The first is a numerical calculation. In this type of computation, the computer provides a numerical solution for a mathematical problem by considering a numerical arrangement as the object of the operation. However, these solutions may not be accurate because a computer may depict errors sometimes.
The second type is symbolic computation. Such a computation often uses a pair of objects and symbolic expressions; this saves it from the inaccuracies caused by error accumulation.
Scientific calculators often work on the first type of computation. Numerical calculations are used to solve questions related to calculus, linear algebra, interpolation and approximation, least-square fitting, and numerical integrals. It is done by employing different computational tools to derive numerical solutions for mathematical problems. When it comes to machine learning, computers can perform numerical calculations to work out Eigenvalues and Eigenvectors of matrices, to solve a set of linear equations and nonlinear equations, and numerical solutions of a differential equation, which are then used for pattern identification, data examination, and automated manufacturing.
On the other hand, symbolic computation is readily applied to build proficient systems used for machine learning. Symbols used can be English letters, numbers, or formulae. A computer processes symbolic data to generate symbolic results. Rounding off values does not cause any issues during a symbolic computation. Without the concern of error accumulation, the system analyzes and evaluates the data to perform the operations and produce an answer, which is valid and closed or numerical with some degree of accuracy. Since the scientific computation systems do not fully support the use of algorithms for machine learning, there is a need for powerful scientific systems, which can carry out complex computations and help various branches of mathematics to shift towards machine learning.
Overview of Scientific Computing Software Platforms
Theoretical argumentation, scientific experiment, and scientific calculations are the three major methods of modern scientific research. Over the years, scientific computing has progressively transformed from being an unconventional method into a mainstream method of scientific research. Owing to the skyrocketing popularity of technology, the use of computer systems has become imperative for scientific computation in a myriad of fields. A number of domains, such as computer programming financial engineering, information retrieval, gene research, environmental simulation, numerical calculation, data analysis, decision support, are making use of these methods extensively. It won’t be wrong to call it the steppingstone for modern scientific research.
Scientific computing is required for machine learning. The study and applications of modern machine learning and the scientific computing platform have been inextricable because mostly the scientific computing applications in the fields involve machine learning algorithms, which make their connection extremely strong. However, the speedy advancement and development in machine learning have become a source of contention for the scientific computing platform.
Popular Scientific Computing Software
Following are the different types of popular scientific computing Machine Learning software:
All those associated with the computer science industry would accept that MATLAB is considered a handy tool for machine learning. MATLAB is a certified program with great performance and execution, which has the tendency to solve approximately all complex programming problems including numerical calculations. When compared with other open-source approved machine learning software like GNU Octave, MATLAB utilizes a variety of algorithms for better and improved performance. MATLAB offers different packages for improved execution of machine learning algorithms, so before experimenting with MATLAB, all options should be observed and practiced comprehensively.
GNU Octave, similar to MATLAB, is free redistribute machine learning software generated by the Free Software Foundation with the assistance of John W. Eaton, guided by a group of volunteers who created GNU Octave, a high-level language, which is compatible with MATLAB, used for resolving numerical calculations, solving lines and nonlinear problems and other mathematical stimulations.
Established by Wolfram Research, Mathematica is an influential computer math system. For individuals working in the fields associated with scientific research, this math system provides a series of mathematical computing functions. It is incorporated computer software that is able to perform numerical calculations, symbolic calculus, and graphics. Moreover, it facilitates people, in different fields, in solving the most complicated hypothetical and practical problems of symbolic and numerical calculation.
Maple is a computer algebra system, which was originated in September 1980, at the University of Waterloo in Canada by a group of researchers but now has been changed into advanced mathematical machine learning software. It has the attributes of strong symbol computing capacity, satisfactory use of the environment, and high precision. Other features of Maple also consist of digital calculations, competent programmable functions, and flexible display of graphics. Maple has dominant and convenient functions in symbolic computation as it solves symbolic formulas given as input and presents the output in mathematical form.
From the prestigious house of IBM, SPSS is a machine learning software for predictive analysis. Its application includes data and text mining, statistical analysis, the prediction mode, and decision optimization. As the brainchild of IBM, SPSS is highly productive. The data is provided in easy-to-comprehend reports with precise data on time. SPSS is beneficial in 5 primary ways. Firstly, it provides business intelligence. Secondly, it meets a company’s expectations with the application of business intelligence. Thirdly, the analysis is simple yet impactful when it comes to controlling data explosions. Next, it ensures maximum user satisfaction. Lastly, it helps a company plan out informed management strategies.
R language is an invention by the “R core team”; it is based on the S language and is primarily used for mapping language, statistical analysis, and environmental operating. Mainly used for statistical analysis or the progress of other statistical related machine learning software, R is a GNU project, which can be used for matrix calculations and also as an application of the S language. It is a command-line operation and is as swift as MATLAB or GNU with the availability of various graphical user interfaces. R language is constructed with a variety of numerical analysis and statistical work, which can be improved through offered packages.
Python is a popular dynamic programming language working with an object-oriented approach. It is an ideal choice for those who intend to work on complex projects. It has a simple but clear syntax, which is very practical for quick coding of programs as well as designing complex software & machine learning software.
NumPy offers a basic science package which comprises of an effective N-dimensional arrangement of objects making use of complex functions, such as C++ and Fortran tools, linear algebra, Fourier transforms, and random number generator functions. It also offers an open-source computing package for science, math, and engineering research work. It works with optimizations, linear algebra, integrals, interpolation, and special function numbers, fast Fourier transform, signal processing, image processing, ordinary differential equation, and similar domains. The most popular drawing library offered by Python is Matplotlib. It is packed with fully functional command APIs that resemble those offered by MATLAB. It is a great drawing tool that can be integrated into GUI applications and provides ideal interactive charting.
A user-friendly interface along with well-designed packages is a much-wanted feature shared by MATLAB, Mathematica, Maple, SPSS, and other machine learning software. However, these can be quite costly and can be easily replaced by some other open-source machine learning software and free engineering computing platforms like the GNU Octave, R, and Python scientific computing packages.
Extensively distributed computing can make use of effective single machine learning algorithms with some alterations. The internet giant, Google, frequently uses Python internally due to its numerous benefits. Moreover, R language can access some of the system functions and comes with a number of well-constructed packages for statistical analysis. Since R functions with the highest visibility are coded in the R language itself, it is important to fully grasp how to apply this language. C, C++, and FORTRAN codes are combined in real-time to accomplish computationally intensive tasks proficiently. Furthermore, RCPP has the ability to merge a rich environment with C/C++ by utilizing R language’s API and data object into class and class’s method for External C++ program.
Even though it faced a number of challenges, Data mining has thrived rapidly by using machine learning algorithms and data to train the systems. The credit for this advancement goes to the engineering structure. In 2014, Cisco introduced an open-source project called OpenSOC. While the source code remained private, its engineering structure was announced. OpenSOC has combined an open-source big data framework with tools for security analysis. This means that its application involves data storage by Hadoop, an open-source machine learning software, real-time indexing by Elasticsearch, and online real-time analysis by Storm.
Raw network traffic, NetFlow, Syslog, and others serve as OpenSOC’s data sources. It can detect a number of security threats by carrying out online and offline data analysis and mining along with external risk intelligence. Apache project has also been taken over by OpenSOC and renamed as Apache Metron. However, the system framework has not been modified extensively.
OpenSOC has the following primary features:
- It is loaded with applications used for analysis and allows the integration of available analytical tools.
- It automatically produces reports and exception alerts.
- It scrutinizes common data sources with the help of an extensible receiver and analyzer.
- It is compatible with ODBC/JDBC and makes use of current analytical tools.
- It supports the HIVE by employing SQL queries for data storage in Hadoop.
- It automates real-time indexed data flows with the help of Elasticsearch.
- It helps with exception detection and rule-based real-time alerts for data streams.
- It aids the collection, storage, and reorganization of the original network packet.
The main components of OpenSOC are the data source system, data collection layer, message system layer, real-time processing layer, storage layer, and analysis processing layer.
Data Source Systems
The data that is employed for the process of data analysis is called a Data source system. It may also be called the data format maintained by the system. Network traffic, files, Syslog, SNMP, and databases are the general sources of data.
Network Traffic
One of the most frequently used data sources is network traffic. It is classified in two major categories: full network traffic and NetFlow. Full network traffic refers to the entire data in a network, including TCP/IP stack data, like TCP header, Mac header, HTTP header, IP header, and HTTP payload data. This enables it to detect attacks on the network in a more proficient manner.
Switch mirroring, optical splittering, and network splittering are the three most common ways to obtain full network traffic. The most inexpensive and convenient method is switch mirroring. Because of these benefits, it is extensively employed in network troubleshooting, simple traffic analysis, and monitoring.
Traffic flow replication is often achieved via optical splittering. In this method, a single ray of light is physically broken into two separate rays by using a highly precise optical fiber production process.
Low cost and high stability are among some of the features that make optical splittering a popular traffic replication method, especially when working with large-scale networks. However, the method also has certain limitations. For instance, optical splittering cannot be used when light undergoes extensive decay. And when there is a specific interface link in the network, it renders the spectrometer useless and you need to turn to network TAP – the traffic replication device.
If you wish to get a view of the network traffic at a session level, NetFlow is meant for you. While it records data for every TCP/IP transaction, it might fail to offer to match the comprehensiveness of full traffic mirroring. However, it offers ease of management and greater readability, if aggregated.
Some common versions of NetFlow are dataflow timestamps, source, and destination IP addresses, source and destination port numbers, input and output interface numbers, next-hop IP addresses, total bytes in the stream, and the number of packets in the stream. NetFlow traffic statistics vary for different versions.
Documents
Documents are probably the simplest form in which data is stored. This includes CSV, XML, JSON, spreadsheets, and different types of log files, like Linux system log, Apache access log, etc.
Syslog
Syslog, a product of the Berkeley Software Distribution Research Center, is considered a benchmark protocol for logging devices in the industry. In a network, it acts as a model used to send system log information. By keeping tabs on all system events, Syslog facilitates the administrator to monitor the system by going through the records generated. With the help of the Syslog process, logging on to system events as well as application operational events has become possible. All it needs to ensure effective communication between machines using the Syslog protocol is the right configuration. These behavior logs can help in analyzing and resolving problems experienced by the machine or network.
For logs sent in the form of Syslog, there exists default support in common network devices, security devices, and Linux distributions. As a master-slave protocol, Syslog delivers a text message to the Syslog receiver. This message is very small, less than 1024 bytes to be exact.
SNMP
SNMP is a standard protocol designed for network management on the basis of the TCP/IP family. It helps in managing the network’s network nodes, such as server, workstation, router, switch, etc. A network administrator using SNMP manages the network more efficiently, maximizes the growth of the network, and detects and addresses the problems in a network at a much faster pace. Moreover, with SNMP, one can also get notification messages and event alerts from network nodes for better network management.
Each SNMP-managed network comprises three primary components: SNMP agent, managed equipment, and network management system (NMS).
Data related to management is gathered and stored on a management information base, which exists on every network unit or node. These network nodes are the devices being managed on the network, such as routers, switches, servers, or hosts that support the SNMP. Using SNMP, the NMS can access this data and provide various processors and storage resources needed for network management. SNMP agents are network management software modules present on a managed device. These agents save necessary management data for the local device and convert it into an SNMP-compatible format to deliver to the NMS.
Database
The data saved in a database is susceptible to abrupt changes. Hence, this data needs to be transferred into the database regularly or on a real-time basis. This can be performed on the basis of JDBC (Java Database Connectivity), which is a Java API used to implement SQL statements. These statements ensure smooth access to multiple relational databases. A database comprises classes and interfaces coded with Java. JDBC acts as a standard on which advanced tools and interfaces are based. These tools are then used by the developers to code various database applications. One can send SQL statements to numerous relational data easily by using JDBC. This means that JDBC saves you from the trouble of writing programs separately for different databases and one program written with JDBC API and capable of sending SQL statements would suffice for Sybase databases, Oracle databases, Informix databases, and any other database.
Reptilian
Reptilian helps access the data that cannot be accessed directly from the database. This lack of access is the consequence of storing data in the third-party corporate system which makes the data susceptible to change at any time. Reptilian accesses the API or catches web pages and files automatically. An example of this can be a situation where a false report is generated when an employee goes on a business or field trip. To resolve this issue, the Reptilian supplies the employee’s travel details or punch card status data from the ERP and gain an access to the control system APIS.
Data Collection Layer
The Data Collection Layer is responsible for gathering data and then processing it into a particular format. Logstash, Flume, Full network traffic collection, and Bro are some applications that are created on the principle of data collection layer. This chapter discusses Logstash only since there is not much difference between Logstash and Flume.
Logstash is one of the best machine learning software design to process data. It is highly efficient in transferring data and processing the format. It offers formatted outputs and well-constructed plug-ins, which facilitates log processing. It comprises of three main components: Inputs, Filters, and Outputs, the details of which are as follows.
The data that is fed in the machine learning software is known as Input. This is the first step. The machine learning software supports a number of different formats, such as:
CloudWatch: Input data taken from Amazon Web Services CloudWatch API
Event Log: Input data taken from reading event log from the Window system
File: Input in the form of reading files
JDBC: Input in the form of data read from a database
Syslog: Input in the Syslog form
The second component is a Filter. The data transfer layer, which deals with formatting, conversion, and filtering of data as well as field addition and modification, is called a Filter. The most commonly used filter is Grok. It is responsible for shaping and resolving the text input with the help of simple parsing and structuring.
The final stage of Logstash is the Output. In this stage, data is delivered at one of the following locations:
Elasticsearch: data is sent to Elasticsearch
File: event data is sent to a disk file.
Kafka: event data is sent to Kafka
Bro
Network traffic analysis provides the basis for a robust framework with a focus on semantic security monitoring. Bro, a passive open-source network traffic analyzer, is responsible for linking all complex suspicious behaviors noticed while monitoring traffic security. It mainly aims to detect any threats of attack and convey background information and usage patterns. Moreover, it creates a visual graph to represent the devices in the network. This helps it keep a close eye on the network traffic and scrutinize network packets. Bro analyzes the network’s full flow mirror in the ML framework and restores the network protocol. It also delivers data to the Kafka cluster via Kafka plug-in. It is important to note that Bro is quite different from conventional intrusion detection or prevention systems. With Bro, the users enjoy an adjustable structure, customizable tools, and detailed monitoring- things that no other conventional system offers to them.
Messaging Layer
The data uses the messaging systems as a superhighway to enter and exit the entire ML framework. Kafka is the most popular messaging system currently in use. This messaging system is of a high-flow, distributed, and publish-subscribe type. Some of its significant features are:
- Messages are continuously delivered through the O (1) disk data structure, which offers durability and stability while storing large quantities of data.
- Millions of messages will easily flow in and out every second, even with simple hardware, due to its high throughput.
- The Kafka server and consumer cluster offer support for partitioning messages.
The parts that make up the Kafka system are stated below:
Broker: One or more servers, called brokers, make up the Kafka cluster.
Topic: Each message posted to the Kafka Cluster falls into a single category, known as a topic. While physically the messages belonging to different topics have separate storage space, conceptually, messages that belong to a particular topic are stored on single or multiple brokers. However, users just assign a topic to the message so as to generate and use the data irrespective of its storage space.
Partition: There are multiple partitions in a single topic. It is a physical idea.
Producer: The Kafka Broker receives the message from the message producer.
Consumer: The person reading the message of the Kafka Broker is called a consumer.
Consumer Group: A consumer is assigned to a single consumer group. This group is specified, but if not, it goes into the default group.
Real-Time Processing Layer
This layer makes use of Storm which is a free of charge real-time computation system having the features of being open-source, distributed, and fault-tolerant.
Storm
Some prominent applications of Storm include real-time analysis, online machine learning, continuous computing, distributed remote invocation, and ETL. It is used in the real-time processing layer. The storm has an upper hand over Hadoop batch processing because of its ability to simplify continuous stream computing and fulfill real-time needs. Moreover, its highly manageable application and impeccable performance make it stand out among the rest of the machine learning software.
Storage Layer
HDFS
HDFS is the first choice for applications dealing with large amounts of data as it offers high throughput data access. While it shares many similarities with a conventional distributed data store, it is also quite unique. General-purpose hardware would be the ideal platform to run HDFS. HDFS is well-suited for cheap machines because of its ability to tolerate major flaws in the system.
The main advantage of using HDFS is the ease of data migration it offers between different platforms. This makes it particularly attractive for applications with large datasets. As a part of a master-slave structure, the HDFS cluster has a name node. This node serves as a master server meant to manage the file namespace and control the client’s access to the files. Moreover, there exists data node/s, one for each machine, which is responsible to store the corresponding node. With the help of HDFS, the file namespace can be accessed by the common people, and the user data can be stored and used in the form of files. To achieve this, HDFS breaks down a file into one or more blocks and stores them in a set of data nodes.
Requests from the file system client are read and written by the data node. Other responsibilities of a data node include block creation, deletion, and block copy instructions from the name node. In order to manipulate a file namespace, including opening, closing, or renaming it, a file or directory operation is utilized. This operation regulates how the block is mapped on to the data node. Name node and data node both work on common machines, usually GNU or Linux.
HDFS’ name or data nodes can be run on any Java-enabled machine because the machine learning software is coded with Java, which is an extremely flexible and convenient language compatible with a variety of machines. Commonly, Java is used in a machine to simply run the name node software, whereas another machine is used to run a data node instance. As per the architecture, a single machine can run instances of multiple data nodes; however, such use is not recommended. A name node acts as the storage space of the arbiter and all HDFA metadata; whereas a user’s real data is read and written without it. The system works smoothly when running a single name node in the cluster.
HBASE
HBASE can be used to construct an extensive organized storage cluster on a low-cost PC server. This shared storage system offers numerous benefits, including greater reliability, better performance, orientation to columns, and scalability. HBASE is an open-source application from Google. Just as Google Bigtable employs GFS as its file storage system; similarly HBASE is also used with Hadoop HDFS to store files. The large amount of data in the Bigtable is processed by MapReduce, whereas HBASE uses Hadoop MapReduce for large data processing and takes advanced language support from Pig and Hive. This makes using statistics on HBASE quite convenient. Moreover, HBASE also enjoys a useful RDBMS data import function offered by Sqoop. This facilitates transferring data from a typical database to HBASE.
Elasticsearch
Open-sourced under the Apache license, Elasticsearch is a famous corporate search engine developed in Java and designed for real-time search in cloud computing. To install Elasticsearch, you would need the latest official version of Java. It is a Lucene-based search service offering a distributed multi-user full-text search engine with a flexible web interface. It is well-liked for its stability, reliability, high-speed, ease-of-installation, and variety of applications.
Installing Marvel
Elasticsearch comes with a free management and monitoring tool called Marvel. Marvel enables the users to use their browsers to directly connect with Elasticsearch, with the help of a collaborative console called Sense. Although installing Marvel is not obligatory, it would improve the engine’s interactivity by running the sample code in the local Elasticsearch cluster. Most of the sample codes available in Elasticsearch online documentation provide links through which they can be viewed in Sense. The link opens on the Sense console with a single click. You can run the below-mentioned command in the Elasticsearch directory to download and install the Marvel connection.
Analytical Processing Layer
Meant for data processing at a large scale, Apache Spark is a quick and multipurpose computing engine. It was designed at UC Berkeley AMP lab as an open-source, general-use parallel framework for Hadoop-like MapReduce. Hadoop MapReduce is undoubtedly an added benefit with Spark; however, it is unique in the sense that the intermediate job output can be saved in memory. A spark is an ideal tool for the iterative MapReduce algorithms for data mining and machine learning as it does not waste time and effort in reading and writing HDFS. While this clustered computing environment shares some similarities with Hadoop, Spark is better at allowing memory-distributed datasets to enhance iterative workloads. It also provides interactive queries with some workloads. The application structure of Spark uses the Scala language. The close incorporation of Scala in Spark allows it to control shared datasets as effortlessly as local collection objects.
Since machine learning algorithms require multi-step iterative computing processing, Spark is a great tool for ML as it is a memory-based computing model which performs multiple steps directly in memory and disks, without modifying the networks unnecessarily. Each computation done with Hadoop’s MapReduce computing framework necessitates reading and writing to disk and a task start. Similarly, ML computations must be performed with an insubstantial error or sufficient convergence with multiple iterations. Consequently, the Input-Output ratio and CPU usage are quite high. Machine learning library (MLlib) is also Spark’s application library, which includes machine learning algorithms, like related tests and data generators. Classification, regression, clustering, and collaborative filtering are the four common machine learning challenges being dealt with with MLlib today. MLlib is designed on the basis of RDD for flawless integration in Spark SQL, GraphX, and Spark Streaming.
Tensor Flow
Researchers and engineers at the Google Brain group, a part of Google’s machine intelligence research institute, originally designed TensorFlow for machine learning and deep learning. However, the potential of this invention made it popular among all fields of computing. TensorFlow is an open-source machine learning software library that performs numerical computation by utilizing data flow diagrams. The diagram comprises nodes, which represent mathematical operations, and lines, which symbolize tensors – a multidimensional arrangement of data related to the nodes. Due to the flexibility of its framework, computing can be branched out into multiple platforms like CPUs or GPUs in desktop computers, servers, mobile devices, etc.
Machine learning started off decades ago and has now become a futuristic, innovative field due to its extensive application. This advancement would not be possible in the absence of two relatively modern trends: substantial data training and well-organized parallel computing. Following are two common examples to understand the impact of well-organized parallel computing:
GPU
GPU stands for Graphics Processing Unit. It was an invention meant to facilitate the provision of computer graphics. It was loaded with thousands of computing units to carry out parallel computing or floating-point, which performs several times better than CPU.
Image classification, video analysis, speech recognition, and natural language, GPU processing on the automatically-driven vehicle are a few of the many revolutionary advancements data scientists have successfully accomplished in machine learning with the help of GPUs.
Deep learning refers to the construction of complex systems with the help of multi-layered, profound neural networks. Currently, a lot of research and capital investment is being done in this domain. Systems designed by deep learning can utilize the large quantities of data for training and identification of features.
Social media giants, huge online enterprises, and prominent research institutes working with data science were the first ones to use GPU accelerators for machine learning. Deep neural networks are trained by the GPU, which encompasses an extensive training set and takes less time and space in the data center framework. These ML training models make use of GPUs to categorize and forecast in the cloud, which leads to the collection of a large amount of data and throughput without using a lot of energy or infrastructure. A machine learning test showed that GPU transcribed pre-recorded speech or multimedia content up to 3 times faster. In comparison to CPU, GPU consists of thousands of computing cores and produces 10 to 100 times application throughput. This has proved that GPU is an undeniable force in this era of big data.
TPU
Designed by Google, TPU is a chip, which makes the deep neural network computing 15 to 30 times faster than the GPU/CPU combination. The computing power improves marginally after off-chip memory access optimization. It lowers computational accuracy and maintains a continuous flow of data.
While designing TPU, Google kept in mind the limited off-chip memory access of GPU and its role in reducing GPU’s energy efficiency ratio. The company invested a lot to provide a lot of memory to TPU. TPU chips use 37% of the chip’s total area. This includes 24 MB of space in the local memory, 6MB of accumulator memory, and docking memory in the master processor.
Another feature, which makes TPU so efficient, is its ability to tolerate low computational accuracy. According to the studies, a low precision operation may lead to the loss of accuracy in the algorithm, which is quite small but of great use in hardware application. Some of the benefits are reduced power usage, faster pace, smaller chip footprint, and lower memory bandwidth needs. Google’s TPU works with 8-bit low-precision computing, which decreases its dependence on transistors at each step. These transistors can perform more operations in the same amount of time because their overall capacity remains unaltered. This takes machine learning algorithms one notch up in terms of complexity, speed, and intelligence.
GPU is relatively slower when retrieving instructions and information for memory. On the other hand, TPU does not even retrieve commands and receives instructions directly from the main processor. In this way, TPU enjoys greater computing proficiency. Data can be reused and multiplied numerous times to achieve the final results of matrix multiplication and convolution.
Hence, most of the data is saved from the last time and only a few of the new data is taken externally next time. This shows that removing all the data on the chip and getting new data would be a wastage of time and effort. TPU ensures a continuous flow of data by shifting data and getting new data only after a clock cycle completes. In this manner, data is reused and memory access times, bandwidth pressure, and the energy consumption is reduced.
