Jack Dongarra and Reed Wade. Linpack Benchmark---Java Version, http://www.netlib.org/benchmark/linpackjava, April 1996.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....using the object oriented features of Java 407 5,145,123 2.96 0.65 Richards2 389 6,012,841 3.62 0.56 Richards3 453 7,188,798 5.91 0.79 Richards4 518 11,247,285 14.2 1.38 Richards5 517 11,247,179 14.2 1.38 Richards6 552 11,247,267 14.2 1. 38 Linpack java version of Linpack [6] 573 9,711,971 0.06 0.06 LinpackOpt hand optimized version [11] 585 8,026,293 0.13 0.00 Table 12. Characterization of benchmark programs. 29 The boxed integers still incur overhead, explaining in part why javac executes slower on Pep. Second, for tight loops, likely to appear in the lexers ....

Jack Dongarra and Reed Wade. Linpack Benchmark---Java Version, http://www.netlib.org/benchmark/linpackjava, April 1996.

....operations such as arithmetic, method calls, object creation and variable accesses. These are useful for highlighting differences between Java environments, but give little useful information about the likely performance of large application codes. Other sets of benchmarks, from both academic [5, 14, 15, 19] and commercial [13, 17, 20] sources, consist primarily of computational kernels, both numeric and non numeric. This type of benchmark is more reflective of application performance, though many of the kernels in these benchmarks are on the small side, both in terms of execution time and memory ....

Dongarra, J. J. and R. Wade, Linpack benchmark: Java version, http://www.netlib.org/benchmark/ linpackjava/

....the address of an array element as an actual argument to a function or subroutine. This is not possible in Java; therefore a Java program cannot pass part of an array starting at a certain offset to a (native library) function. One way round this restriction, applied in the Java Linpack benchmark [9, 2], is to add one integer offset argument for each array argument of a function. The JCI generated bindings support a more elegant solution as well, which does not involve extra arguments to native library functions. The elements of an array arr of any type starting at offset i can be passed to a ....

....7 communication, and all computations are done by the benchmark program. 6 DISCUSSION AND RELATED WORK Until the Java compiler technology reaches maturity, the use of native numerical code in Java programs is certain to improve performance, as recent experiments with the Java Linpack benchmark [9] and several BLAS Level 1 functions written in C have shown [2] By binding the original native libraries like BLAS, Java programs can gain in performance on all those hardware platforms where the libraries are efficiently implemented. A Java to PVM interface is publicly available [19] A Java ....

J. Dongarra and R. Wade. Linpack benchmark -- Java version. http://www.netlib.org/benchmark/linpackjava, 1996.

....operations such as arithmetic, method calls, object creation and variable accesses. These are useful for highlighting differences between Java environments, but give little useful information about the likely performance of large application codes. Other sets of benchmarks, from both academic [5, 14, 15, 19] and commercial [13, 17, 20] sources, consist primarily of computational kernels, both numeric and non numeric. This type of benchmark is more reflective of application performance, though many of the kernels in these benchmarks are on the small side, both in terms of execution time and memory ....

Dongarra, J. J. and R. Wade, Linpack benchmark: Java version, http://www.netlib.org/benchmark/ linpackjava/

....thus has a negligible share in the breakdown of the total execution time for the Java version of IS. Clearly the JavaMPI binding does not introduce a noticeable overhead. 4 Related work One of the few other scientific benchmarks in Java that we know of is the sequential Java version of Linpack [5, 6]. It is packaged in a fancy applet that entices web surfers into running the benchmark on their own machines, thus demonstrating the appeal of Java cross network portability. A number of low level Java benchmarks have also been created; they are useful for comparing Java implementations, but ....

J. Dongarra and R. Wade. Linpack benchmark -- Java version. http://www.netlib.org/benchmark/linpackjava.

....the address of an array element as an actual argument to a function or subroutine. This is not possible in Java; therefore a Java program cannot pass part of an array starting at a certain offset to a (native library) function. One way round this restriction, applied in the Java Linpack benchmark [7, 2], is to add one integer offset argument for each array argument of a function. The JCI generated bindings support a more elegant solution as well, which does not involve extra arguments to native library functions. The elements of an array arr of any type starting at offset i can be passed to ....

....for communication, and all computations are done by the benchmark program. 6 Discussion and related work Until the Java compiler technology reaches maturity, the use of native numerical code in Java programs is certain to improve performance, as recent experiments with the Java Linpack benchmark [7] and several BLAS Level 1 functions written in C have shown [2, 15] By binding the original native libraries like BLAS, Java programs can gain in performance on all those hardware platforms where the libraries are efficiently implemented. A Java to PVM interface is publicly available [19] A ....

J. Dongarra and R. Wade. Linpack benchmark -- Java version. http://www.netlib.org/benchmark/linpackjava.

....As our experiments on IBM SP2 machines so far have shown, a negligible amount of time is spent in the binding itself during execution of Java programs. The use of native numerical code in Java programs is certain to improve performance, as recent experiments with the Java Linpack benchmark [7] and some BLAS Level 1 functions written in C have shown [2, 12] By binding the original native libraries like BLAS, Java programs can gain in performance on all those hardware platforms where the libraries are efficiently implemented. 3 Experimental results In order to evaluate the performance ....

J. Dongarra and R. Wade. Linpack benchmark -- Java version. http://www.netlib.org/benchmark/linpackjava.

....of these routines for performance comparisons. Java implementations of the BYTEmark benchmarks [2] include routines for sorting, prime number sieving, computing Fourier coefficients, and matrix factorization [19] There is also a Java implementation of the classic Linpack linear algebra benchmark [5]. Source code, and comparable programs in C, C or Fortran, are available for all these benchmarks, so these are the most useful of the currently available Java benchmarks for analysing the performance of Java for scientific applications. 3 Java Grande Forum Benchmarks The Java Grande Forum is ....

Jack Dongarra and Reed Wade, Linpack Benchmark -- Java version, http://www.netlib.org/benchmark/linpackjava/.

....250 300 s. It should be noted that these results compare codes running directly under the operating systems with those running in the JVM. For example, according to [6] a single 200 MHz PentiumPro will achieve in excess of 62 Mflop s on a Fortran version of LinPack. A test of the Java LinPack code [7] gave a peak performance of 22 Mflop s for the same processor running the JVM. The difference in performance will account for much of the additional overhead that mpiJava imposes on C MPI codes. From this it can be deduced that the quality and performance of JVM on each platform will have the ....

Jack Dongarra and Reed Wade. Linpack benchmark---Java version. http://www.netlib.org/benchmark/linpackjava/.

....and its current implementation and status. 1 Introduction Real programmers program in Fortran, and can do so in any language. Ian Graham, 1994 [1] Popular opinion seems to hold the somewhat erroneous view that Java is too slow for numerical programming. However, the Java Linpack benchmark [2] has recorded excellent floating point arithmetic speeds (68.6 Mflop) on a PC resulting from Just In Time (JIT) compilation of Java class files. Also, there are many small to intermediate scale problems where speed is not an issue. For instance, physical quantities such as permeability, stress and ....

.... 1 aoffset] a[ j) 1 aoffset] 0) Then the adapter makes the actual call: private static int cfunmethcall( java.lang.reflect.Method funcptr, int arg0 , int arg1 ) throws java.lang.reflect.InvocationTargetException, java.lang.IllegalAccessException Object [ funcargs = new Object [2]; int retval; funcargs[0] new Integer(arg0) funcargs[1] new Integer(arg1) retval = Integer) funcptr.invoke(null,funcargs) intValue( return retval; 2.2 Implementation of the f2j compiler The program f2c is a horror, based on ancient code and hacked unmercifully. Users are only ....

J. Dongarra and R. Wade. Linpack Benchmark -- Java Version. [Online] Available http://www.netlib.org/benchmark/linpackjava, April 1996.

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