Lecture 2c: Benchmarks

1. Lecture 2c:Benchmarks

2. Benchmarking Benchmark is a program that is run on a computer to measure its performance and compare it with other machines Best benchmark is the users’ workload – the mixture of programs and operating system commands that users run on a machine.  Not practical Standard benchmarks

3. Benchmarking Types of Benchmarks Synthetic benchmarks Toy benchmarks Microbenchmarks Program Kernels Real Applications

4. Benchmarking Synthetic benchmarks Artificially created benchmark programs that represent the average frequency of operations (instruction mix) of a large set of programs Whetstone benchmark Dhrystone benchmark Rhealstone benchmark

5. Benchmarking Synthetic benchmarks Whetstone benchmark First written in Algol60 in 1972, today Fortran, C/C++, Java versions are available Represents the workload of numerical applications Measures floating point arithmetic performance Unit is Millions of Whetstone instructions per second (MWIPS) Shortcommings: Does not represent constructs in modern languages, such as pointers, etc. Does not consider cache effects

6. Benchmarking Synthetic benchmarks Dhrystone benchmark First written in Ada in1984, today Represents the workload of C version is available Statistics are collected on system software, such as operating system, compilers, editors and a few numerical programs Measures integer and string performance, no floating-point operations Unit is the number of program iteration completions per second Shortcommings: Does not represent real life programs Compiler optimization overstates system performance Small code that may fit in the instruction cache

7. Benchmarking Synthetic benchmarks Rhealstone benchmark Multi-tasking real-time systems Factors are: Task switching time Pre-emption time Interrupt latency time Semaphore shuffling time Dead-lock breaking time Datagram throughput time Metric is Rhealstones per second 6 ∑ wi . (1/ ti) i=1

8. Benchmarking Toy benchmarks 10-100 lines of code that the result is known before running the toy program Quick sort Sieve of Eratosthenes Finds prime numbers http://upload.wikimedia.org/wikipedia/commons/8/8c/New_Animation_Sieve_of_Eratosthenes.gif • func sieve( var N ) • var PrimeArray as array of size N • initialize PrimeArray to all true • for i from 2 to N • for each j from i + 1 to N, where i divides j • set PrimeArray( j ) = false

9. Benchmarking Microbenchmarks Small, specially designed programs used to test some specific function of a system (eg. Floating-point execution, I/O subsystem, processor-memory interface, etc.) Provide values for important parameters of a system Characterize the maximum performance if the overall performance is limited by that single component

10. Benchmarking Kernels Key pieces of codes from real applications. LINPACK and BLAS Livermore Loops NAS

11. Benchmarking Kernels LINPACK and BLAS Libraries LINPACK – linear algebra package Measures floating-point computing power Solves system of linear equations Ax=b with Gaussian elimination Metric is MFLOP/s DAXPY - most time consuming routine Used as the measure for TOP500 list BLAS – Basic linear algebra subprograms LINPACK makes use of BLAS library

12. Benchmarking Kernels LINPACK and BLAS Libraries SAXPY – Scalar Alpha X Plus Y Y = a X + Y, where X and Y are vectors, a is a scalar SAXPY for single and DAXPY for double precision Generic implementation: for (int i = m; i < n; i++) { y[i] = a * x[i] + y[i]; }

13. Benchmarking Kernels Livermore Loops Developed at LLNL Originally in Fortran, now also in C 24 numerical application kernels, such as: hydrodynamics fragment, incomplete Cholesky conjugate gradient, inner product, banded linear systems solution, tridiagonal linear systems solution, general linear recurrence equations, first sum, first difference, 2-D particle in a cell, 1-D particle in a cell, Monte Carlo search, location of a first array minimum, etc. Metrics are arithmetic, geometric and harmonic mean of CPU rate

14. Benchmarking Kernels NAS Parallel Benchmarks Developed at NASA Advanced Supercomputing division Paper-and-pencil benchmarks 11 benchmarks, such as: Discrete Poisson equation, Conjugate gradient Fast Fourier Transform Bucket sort Embarrassingly parallel Nonlinear PDE solution Data traffic, etc.

15. Benchmarking Real Applications Programs that are run by many users C compiler Text processing software Frequently used user applications Modified scripts used to measure particular aspects of system performance, such as interactive behavior, multiuser behavior

16. Benchmarking Benchmark Suites Desktop Benchmarks SPEC benchmark suite Server Benchmarks SPEC benchmark suite TPC Embedded Benchmarks EEMBC

17. Benchmarking SPEC Benchmark Suite Desktop Benchmarks CPU-intensive SPEC CPU2000 11 integer (CINT2000) and 14 floating-point (CFP2000) benchmarks Real application programs: C compiler Finite element modeling Fluid dynamics, etc. Graphics intensive SPECviewperf Measures rendering performance using OpenGL SPECapc Pro/Engineer – 3D rendering with solid models Solid/Works – 3D CAD/CAM design tool, CPU-intensive and I/O intensive tests Unigraphics – solid modeling for an aircraft design Server Benchmarks SPECWeb – for web servers SPECSFS – for NFS performance, throughput-oriented

18. Benchmarking TPC Benchmark Suite Server Benchmark Transaction processing (TP) benchmarks Real applications TPC-C: simulates a complex query environment TPC-H: ad hoc decision support TPC-R: business decision support system where users run a standard set of queries TPC-W: business-oriented transactional web server Measures performance in transactions per second. Throughput performance is measured only when response time limit is met. Allows cost-performance comparisons

19. Benchmarking EEMBC Benchmarks for embedded computing systems 34 benchmarks from 5 different application classes: Automotive/industrial Consumer Networking Office automation Telecommunications

20. Benchmarking Benchmarking Strategies Fixed-computation benchmarks Fixed-time benchmarks Variable-computation and variable-time benchmarks

21. Benchmarking Benchmarking Strategies Fixed-computation benchmarks Fixed-time benchmarks Variable-computation and variable-time benchmarks

22. Benchmarking Fixed-Computation benchmarks W: fixed workload (number of instructions, number of floating-point operations, etc) T: measured execution time R: speed Compare

23. Benchmarking Fixed-Computation benchmarks Amdahl’s Law

24. Benchmarking Fixed-Time benchmarks On a faster system, a larger workload can be processed in the same amount of time T: fixed execution time W: workload R: speed Compare

25. Benchmarking Fixed-Time benchmarks Scaled Speedup

26. Benchmarking Variable-Computation and Variable-Time benchmarks In this type of benchmark, quality of the solution is improved. Q: quality of the solution T: execution time Quality improvements per second: