Diagnóstico de la situación

Workload Description

The scientific workload , called SCIENCE, is a suite of multistream (homogeneous) batch jobs. These jobs are we ll-known programs frequently used i n science and research environments. Four benchmarks commonly used in physics are ISA­ JET and GEISHA, two Monte Carlo simulations used in high-energy physics applications, and TAIR and 1WING , two tests used in aerodynamics applications. Three other programs used in chemistry are GAUSSIAN 8 2 , a quantum chem­ istry package ; MOPAC, a general-purpose semi­ empirical molecu Jar orbital package; and RS/ 1 , an interactive data ana lysis software package fre­ quently used i n chemistry labs.

Performance Metric for SCIENCE Workload

The most important performance metric is throughput. Throughput is defi ned as the num-

Digital Technical journal No. 5 Septem ber 1987

ber of jobs that the system can process in a given time. This metric was derived in the fol lowing manner, using the elapsed times extracted from the batch log fi les. For a closed system with one job,

1

Throughput = .

Average elapsed ume

The following steps were used tO apply this equation tO the multinode, multistream system:

Sum of elapsed Average elapsed _ ti mes for all

time per job Total number of jobs in which Total number of jobs = Number of nodes X Number of streams, and

Th roug put - Average elapsed time per job h _ Total number of

The SCIENCE workload is a su ite of repre­ sentative programs, each yielding a throughput for each system. To compare the performance of systems u nder this workload, the multiple relative performances based on the individual throughput comparison have to be aggregated . The geometric mean is chosen tO aggregate the relative performances, with equal weight on each program 4 ·5

Test Methodology

The basic methodology of this study was tO increase the load on the system gradually until the processors were ful ly uti lized , thus yield ing a peak throughput for a particular configuration . Since all the benchmarks were run as batch jobs, this saturation was achieved using multistream batch jobs. Up to five batch streams on each pro­ cessor were run for each benchmark tested.

Potential 1/0 and memory bottlenecks were m inim ized by allowing large si zes of user work­ ing sets and by al locating one d isk per job stream for data and scratch fi les.

Hardware and Software Configuration The hardware environment consisted of the fol­ lowing elements:

• A VAX 8700 system with one CPU, two HSC70

stOrage control lers, and two SA4 82 storage arrays

• A VAX 8974 system with four VAX 8700 CPUs, two HSC70 stOrage controllers, and six SA4 82 storage arrays

8 1

VAXcluster Systems

System Level Performance of VAX 8974 and 8978 Systems

• A VAX 8978 system with eight VAX 8700 CPUs, fou r HSC70 storage controllers, and twelve SA4 82 storage arrays

The software environment consisted of the VAXjVMS version 4 . 4 operating system and FORTRAN version 4 . 3 .

Characterization ofthe SCIENCE Workload

The seven benchmarks of the SCIENCE workload were grouped into two categories based on their I/0 behavior. One group included the bench­ marks with virtually no I/0 activity; the other with those that generated some I/0 activity.

MOPAC and TWING both generate few IjOs, thereby falling i nto the first category. The re­ maining five benchmarks, ISA)ET, GEISHA, TAI R, RS/ 1 , and GAUSSIAN 82 exhibit some I/0 activ­ ity. Among a l l , GAUSSIAN 82 is the most 1/0 intensive . MOPAC and GAUSSIAN 82 were chosen as being representative of each category. Before starting the experiments, we ran the representa­ tive benchmarks on a VAX 8700 system to study the characteristics of the system resource usage. The following graphs give a profil e of the two cat­ egories in terms of these stud ies .

Figure 1 shows the profi les of MOPAC and GAUSSIAN 82 in terms of processor utilization plotted against elapsed time . Note that a single stream of MOPAC saturated the VAX 8700 proces­ sor during the entire run of a l most 40 minutes, doing virtual ly no I/0. On the other hand , GAUS­ SIAN 82 consumed the most CPU power in the first five minutes and then remained at a lower rate (67 percent) of CPU utilization for the rest of the run time . For the first five minutes , GAUS- a _{1 00} UJ N :J 80 i= ::J ::J ₆₀ c.. 0 LJ._ 40 0 f- z UJ ₂₀ 0 cr: UJ 0 c.. 0 5 1 0 1 5 20 25 30 35 40

ELAPSED TIME (MINUTES) KEY:

"' MOPAC

D GAUSSIAN82

Figure 1 Transient CPU Utilization

82

SIAN 82 generated little ljO activity. Then , how­ ever, it generated a heavy I/0 load - up to 2 1 IjOs per second - to the user disk during the rest of the run. The ljO transfer size of GAUSSIAN 8 2 is the largest of a l l the tests, around 2 5 kilobytes ( KB) per request. The I/0 data rate of a single GAUSSl.A.!'\1 82 test , col lected using the Software Performance Monitor (SPM) program with 60-second intervals, shows as much as

530KB per second during this IjO i ntensive period .

Results and Observations

MOPAC Results. Figure 2 p lots the throughput of the MOPAC benchmark against the total num­ ber of streams i n the cl uster. The throughput increases linearly up to one job stream per pro­ cessor. Beyond this point the curves remain flat . This flattening occurs because the benchmark is very CPU intensive, and one stream saturates a single processor with an average utilization of 9 9 . 6 percent. Therefore, adding more streams does not increase throughpu t.

The throughputs at which the curves flatten out are 1 .6 , 6 . 4 , and 1 2 . 8 jobs per hour re­ spectively for the VAX 8 7 0 0 , VAX 8974, and VAX 8978 systems. In terms of relative perfor­ mance , the throughput of the VAX 8974 and VAX 8978 systems were 4 . 0 times and 8 . 0 times respectively greater than the throughput of a sin­ gle VAX 8700 CPU, a l l showing linear growth with the number of streams.

GAUSSIAN 82 Results

Figure 3 shows the throughput for the GAUS­ SIAN 8 2 benchmark plotted against the total number of concurrent streams on all the systems.

ir 1 6 ::J 0 1 4 I (jj 1 2 Ill 0 1 0 2 f- 8 ::J c.. 6 I C) 4 ::J 0 ₂ cr: I f- 5 KEY: • VAX 8978 ... VAX 8974 0 VAX 8700 Figure 2 1 0 1 5 20 25 TOTAL STREAMS MOPA C Throughput

Digital Technical ]om-nal

The curves show how throughput grows as the