Table of Contents
Performance Benchmarks
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Definition and Purpose
In computing, performance benchmarks are standardized tests used to evaluate and compare the performance of hardware and software systems. They provide objective metrics that help determine how well a system performs specific tasks or functions. Benchmarks are essential for assessing system capabilities, optimizing performance, and making informed decisions about upgrades and configurations.
Types of Benchmarks
Performance benchmarks can be categorized into various types, each focusing on different aspects of system performance:
- **Real-World Benchmarks:** These tests evaluate system performance using actual applications and tasks. They provide insights into how a system performs under typical usage conditions. Examples include benchmarking video editing software or gaming performance.
- **Microbenchmarks:** These tests focus on specific components or operations within a system, such as processor speed or memory latency. They provide detailed information about particular aspects of performance.
Metrics Used in Benchmarks
Benchmarks use various metrics to quantify system performance, including:
- **Processing Speed:** Measures how quickly a system can execute instructions or complete tasks, often expressed in terms of clock speed (GHz) or operations per second.
- **Throughput:** Indicates the amount of data a system can process within a given time frame, such as transactions per second or data transfer rates.
- **Latency:** Measures the time it takes for a system to respond to a request or complete an operation, often expressed in milliseconds (ms) or microseconds (µs).
- **Efficiency:** Evaluates how well a system uses its resources, such as CPU, memory, and storage, to achieve desired performance outcomes.
Applications of Benchmarks
Performance benchmarks are used in various contexts, including:
- **Hardware Evaluation:** Benchmarks help compare the performance of different hardware components, such as processors, GPUs, and storage devices. They aid in selecting the best hardware for specific applications and workloads.
- **Software Optimization:** Developers use benchmarks to identify performance bottlenecks and optimize software code for better efficiency and speed.
- **System Configuration:** Benchmarks assist in configuring systems for optimal performance by providing insights into how different settings and components affect overall performance.
Benchmarking Tools and Software
Numerous tools and software applications are available for conducting performance benchmarks, including:
- ** SPEC CPU:** A widely used benchmark suite for evaluating processor performance through a range of synthetic and real-world tests.
- ** PassMark PerformanceTest:** A benchmarking tool that measures various aspects of system performance, including CPU, GPU, memory, and storage.
- ** 3DMark:** A benchmarking tool specifically designed for assessing the performance of graphics cards and gaming systems.
Challenges and Limitations
Benchmarking can present challenges and limitations, such as:
- **Variability:** Performance results can vary based on system configurations, operating conditions, and workload characteristics. Ensuring consistency and repeatability in benchmarks is crucial for accurate comparisons.
- **Relevance:** Benchmarks may not always reflect real-world performance for all applications or use cases. It is important to consider how benchmarks align with actual usage scenarios.
- **Bias:** Some benchmarks may favor specific hardware or software, potentially leading to biased results. Using a variety of benchmarks can help mitigate this issue.
Benchmarking in Performance Tuning
Performance tuning involves adjusting system settings and configurations to achieve optimal performance based on benchmark results. By analyzing benchmark data, system administrators and developers can identify areas for improvement and implement changes to enhance overall performance. Performance tuning is an iterative process that may require multiple rounds of benchmarking and adjustments.
Industry Standards and Benchmarks
Industry standards and benchmarks play a key role in ensuring consistency and comparability across different systems and vendors. Organizations such as SPEC and WPC (World Programming Championship) develop and maintain standardized benchmark tests that are widely recognized and used within the industry.
Future Trends in Benchmarking
As technology evolves, benchmarking methodologies and tools continue to advance. Future trends may include:
- **Integration with Artificial Intelligence (AI):** AI-driven benchmarks could provide more sophisticated analyses of system performance and usage patterns.
- **Benchmarking in Emerging Technologies:** New benchmarks may be developed for emerging technologies such as quantum computing and edge computing, addressing unique performance characteristics and requirements.
Impact on Decision-Making
Benchmarking results have a significant impact on decision-making in computing, influencing choices related to hardware purchases, software development, and system configurations. Accurate and reliable benchmarks provide valuable insights that help organizations and individuals make informed decisions to achieve desired performance outcomes.
Best Practices for Benchmarking
To achieve meaningful and accurate benchmarking results, consider the following best practices:
- **Use Multiple Benchmarks:** Employ a variety of benchmarks to obtain a comprehensive view of system performance.
- **Ensure Consistency:** Conduct benchmarks under consistent conditions to ensure reliable and comparable results.
- **Analyze Results Thoroughly:** Interpret benchmark data in the context of specific use cases and performance goals.
- Snippet from Wikipedia: Benchmarking
Benchmarking is the practice of comparing business processes and performance metrics to industry bests and best practices from other companies. Dimensions typically measured are quality, time and cost.
Benchmarking is used to measure performance using a specific indicator (cost per unit of measure, productivity per unit of measure, cycle time of x per unit of measure or defects per unit of measure) resulting in a metric of performance that is then compared to others.
Also referred to as "best practice benchmarking" or "process benchmarking", this process is used in management in which organizations evaluate various aspects of their processes in relation to best-practice companies' processes, usually within a peer group defined for the purposes of comparison. This then allows organizations to develop plans on how to make improvements or adapt specific best practices, usually with the aim of increasing some aspect of performance. Benchmarking may be a one-off event, but is often treated as a continuous process in which organizations continually seek to improve their practices.
In project management benchmarking can also support the selection, planning and delivery of projects.
In the process of best practice benchmarking, management identifies the best firms in their industry, or in another industry where similar processes exist, and compares the results and processes of those studied (the "targets") to one's own results and processes. In this way, they learn how well the targets perform and, more importantly, the business processes that explain why these firms are successful. According to National Council on Measurement in Education, benchmark assessments are short assessments used by teachers at various times throughout the school year to monitor student progress in some area of the school curriculum. These also are known as interim government.
In 1994, one of the first technical journals named Benchmarking was published.
Performance: Systems performance, Systems performance bibliography, Systems Performance Outline: (Systems Performance Introduction, Systems Performance Methodologies, Systems Performance Operating Systems, Systems Performance Observability Tools, Systems Performance Applications, Systems Performance CPUs, Systems Performance Memory, Systems Performance File Systems, Systems Performance Disks, Systems Performance Network, Systems Performance Cloud Computing, Systems Performance Benchmarking, Systems Performance perf, Systems Performance Ftrace, Systems Performance BPF, Systems Performance Case Study), Accuracy, Algorithmic efficiency (Big O notation), Algorithm performance, Amdahl's Law, Android performance, Application performance engineering, Async programming, Bandwidth, Bandwidth utilization, bcc, Benchmark (SPECint and SPECfp), BPF, bpftrace, Performance bottleneck (“Hotspots”), Browser performance, C performance, C Plus Plus performance | C++ performance, C Sharp performance | performance, Cache hit, Cache performance, Capacity planning, Channel capacity, Clock rate, Clojure performance, Compiler performance (Just-in-time (JIT) compilation - Ahead-of-time compilation (AOT), Compile-time, Optimizing compiler), Compression ratio, Computer performance, Concurrency, Concurrent programming, Concurrent testing, Container performance, CPU cache, CPU cooling, CPU cycle, CPU overclocking (CPU boosting, CPU multiplier), CPU performance, CPU speed, CPU throttling (Dynamic frequency scaling - Dynamic voltage scaling - Automatic underclocking), CPU time, CPU load - CPU usage - CPU utilization, Cycles per second (Hz), CUDA (Nvidia), Data transmission time, Database performance (ACID-CAP theorem, Database sharding, Cassandra performance, Kafka performance, IBM Db2 performance, MongoDB performance, MySQL performance, Oracle Database performance, PostgreSQL performance, Spark performance, SQL Server performance), Disk I/O, Disk latency, Disk performance, Disk speed, Disk usage - Disk utilization, Distributed computing performance (Fallacies of distributed computing), DNS performance, Efficiency - Relative efficiency, Encryption performance, Energy efficiency, Environmental impact, Fast, Filesystem performance, Fortran performance, FPGA, Gbps, Global Interpreter Lock - GIL, Golang performance, GPU - GPGPU, GPU performance, Hardware performance, Hardware performance testing, Hardware stress test, Haskell performance, High availability (HA), Hit ratio, IOPS - I/O operations per second, IPC - Instructions per cycle, IPS - Instructions per second, Java performance (Java data structure performance - Java ArrayList is ALWAYS faster than LinkedList, Apache JMeter), JavaScript performance (V8 JavaScript engine performance, Node.js performance - Deno performance), JVM performance (GraalVM, HotSpot), Kubernetes performance, Kotlin performance, Lag (video games) (Frame rate - Frames per second (FPS)), Lagometer, Latency, Lazy evaluation, Linux performance, Load balancing, Load testing, Logging, macOS performance, Mainframe performance, Mbps, Memory footprint, Memory speed, Memory performance, Memory usage - Memory utilization, Micro-benchmark, Microsecond, Monitoring
Linux/UNIX commands for assessing system performance include:
- uptime the system reliability and load average
- Top (Unix) | top for an overall system view
- Vmstat (Unix) | vmstat vmstat reports information about runnable or blocked processes, memory, paging, block I/O, traps, and CPU.
- Htop (Unix) | htop interactive process viewer
- dstat, atop helps correlate all existing resource data for processes, memory, paging, block I/O, traps, and CPU activity.
- iftop interactive network traffic viewer per interface
- nethogs interactive network traffic viewer per process
- iotop interactive I/O viewer
- Iostat (Unix) | iostat for storage I/O statistics
- Netstat (Unix) | netstat for network statistics
- mpstat for CPU statistics
- tload load average graph for terminal
- xload load average graph for X
- /proc/loadavg text file containing load average
(Event monitoring - Event log analysis, Google Cloud's operations suite (formerly Stackdriver), htop, mpstat, macOS Activity Monitor, Nagios Core, Network monitoring, netstat-iproute2, proc filesystem (procfs)]] - ps (Unix), System monitor, sar (Unix) - systat (BSD), top - top (table of processes), vmstat), Moore’s law, Multicore - Multi-core processor, Multiprocessor, Multithreading, mutex, Network capacity, Network congestion, Network I/O, Network latency (Network delay, End-to-end delay, packet loss, ping - ping (networking utility) (Packet InterNet Groper) - traceroute - netsniff-ng, Round-trip delay (RTD) - Round-trip time (RTT)), Network performance, Network switch performance, Network usage - Network utilization, NIC performance, NVMe, NVMe performance, Observability, Operating system performance, Optimization (Donald Knuth: “Premature optimization is the root of all evil), Parallel processing, Parallel programming (Embarrassingly parallel), Perceived performance, Performance analysis (Profiling), Performance design, Performance engineer, Performance equation, Performance evaluation, Performance gains, Performance Mantras, Performance measurement (Quantifying performance, Performance metrics), Perfmon, Performance testing, Performance tuning, PowerShell performance, Power consumption - Performance per watt, Processing power, Processing speed, Productivity, Python performance (CPython performance, PyPy performance - PyPy JIT), Quality of service (QOS) performance, Refactoring, Reliability, Response time, Resource usage - Resource utilization, Router performance (Processing delay - Queuing delay), Ruby performance, Rust performance, Scala performance, Scalability, Scalability test, Server performance, Size and weight, Slow, Software performance, Software performance testing, Speed, Stress testing, SSD, SSD performance, Swift performance, Supercomputing, Tbps, Throughput, Time (Time units, Nanosecond, Millisecond, Frequency (rate), Startup time delay - Warm-up time, Execution time), TPU - Tensor processing unit, Tracing, Transistor count, TypeScript performance, Virtual memory performance (Thrashing), Volume testing, WebAssembly, Web framework performance, Web performance, Windows performance (Windows Performance Monitor). (navbar_performance)
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