Understand measures of supercomputer performance and storage system capacity
On this page:
- Measure computer performance in FLOPS
- Measure storage capacity in bytes
- Prefixes for representing orders of magnitude
- Understand orders of magnitude in computer performance
- Understand orders of magnitude in storage capacity
- IU examples
Measure computer performance in FLOPS
The performance capabilities of supercomputers (for example, Indiana University's research supercomputers) are expressed using a standard rate for indicating the number of floating-point arithmetic calculations systems can perform on a per-second basis. The rate, floating-point operations per second, is abbreviated as FLOPS.
Computer vendors and service providers typically list the theoretical peak performance (Rpeak) capabilities of their systems expressed in FLOPS. A system's Rpeak is calculated by multiplying the number of processors by the clock speed of the processors, and then multiplying that product by the number of floating-point operations the processors can perform in one second on standard benchmark programs, such as the LINPACK DP TPP and HPC Challenge (HPCC) benchmarks, and the SPEC integer and floating-point benchmarks.
Measure storage capacity in bytes
Computer storage and memory capacities are expressed in units called bits and bytes. A bit is the smallest unit of measurement for digital information in computing. A byte is the number of bits a particular computing architecture needs to store a single text character. Consequently, the number of bits in a byte can differ between computing platforms. However, due to the overwhelming popularity of certain major computing platforms, the 8-bit byte has become the international standard, as defined by the International Electrotechnical Commission (IEC).
An uppercase "B" is used for abbreviating "byte(s)"; a lowercase "b" is used for abbreviating "bit(s)". This difference can cause confusion. For example, file sizes are commonly represented in bytes, but download speeds for electronic data are commonly represented in bits per second. With a download speed of 10 megabits per second (Mbps), you might mistakenly assume a 100 MB file will download in only 10 seconds. However, 10 Mbps is equivalent to only 1.25 MB per second, meaning a 100 MB file would take at least 80 seconds to download.
Prefixes for representing orders of magnitude
Orders of magnitude (in base 10) are expressed using standard metric prefixes, which are abbreviated to single characters when prepended to other abbreviations, such as FLOPS and B (for byte):
Prefix | Abbreviation | Order of magnitude (as a factor of 10) |
Computer performance | Storage capacity |
---|---|---|---|---|
giga- | G | 10^{9} | gigaFLOPS (GFLOPS) |
gigabyte (GB) |
tera- | T | 10^{12} | teraFLOPS (TFLOPS) |
terabyte (TB) |
peta- | P | 10^{15} | petaFLOPS (PFLOPS) |
petabyte (PB) |
exa- | E | 10^{18} | exaFLOPS (EFLOPS) |
exabyte (EB) |
zetta- | Z | 10^{21} | zettaFLOPS (ZFLOPS) |
zettabyte (ZB) |
yotta- | Y | 10^{24} | yottaFLOPS (YFLOPS) |
yottabyte (YB) |
- Terascale: Refers to methods and processes for using supercomputers capable of performing at least 1 TFLOPS or storage systems capable of storing at least 1 TB
- Petascale: Refers to methods and processes for using supercomputers capable of performing at least 1 PFLOPS or storage systems capable of storing at least 1 PB
- Exascale: Refers to methods and processes for using supercomputers capable of performing at least 1 EFLOPS or storage systems capable of storing at least 1 EB
Understand orders of magnitude in computer performance
GigaFLOPS
A 1 gigaFLOPS (GFLOPS) computer system is capable of performing one billion (10^{9}) floating-point operations per second. To match what a 1 GFLOPS computer system can do in just one second, you'd have to perform one calculation every second for 31.69 years.
TeraFLOPS
A 1 teraFLOPS (TFLOPS) computer system is capable of performing one trillion (10^{12}) floating-point operations per second. The rate 1 TFLOPS is equivalent to 1,000 GFLOPS. To match what a 1 TFLOPS computer system can do in just one second, you'd have to perform one calculation every second for 31,688.77 years.
PetaFLOPS
A 1 petaFLOPS (PFLOPS) computer system is capable of performing one quadrillion (10^{15}) floating-point operations per second. The rate 1 PFLOPS is equivalent to 1,000 TFLOPS. To match what a 1 PFLOPS computer system can do in just one second, you'd have to perform one calculation every second for 31,688,765 years.
ExaFLOPS
A 1 exaFLOPS (EFLOPS) computer system is capable of performing one quintillion (10^{18}) floating-point operations per second. The rate 1 EFLOPS is equivalent to 1,000 PFLOPS. To match what a 1 EFLOPS computer system can do in just one second, you'd have to perform one calculation every second for 31,688,765,000 years.
Understand orders of magnitude in storage capacity
Gigabyte
A gigabyte is equal to one billion bytes. You can fit 4.7 GB of data on one single-sided DVD (each DVD is about 1.2 mm, or 0.047 inches, thick).
Terabyte
A terabyte is equal to one trillion (one thousand billion) bytes, or 1,000 GB. To hold 1 TB of data, you would need about 213 single-sided DVDs (a stack that's about 255.6 mm, or 10.06 inches, tall).
Petabyte
A petabyte is equal to one quadrillion (one thousand trillion) bytes, or 1,000 TB. To hold 1 PB of data, you would need about 212,766 single-sided DVDs (a stack that's about 255.3 meters, or 837.67 feet, tall).
Exabyte
An exabyte is equal to one quintillion (one thousand quadrillion) bytes, or 1,000 PB. To hold 1 EB, you would need about 212,765,958 single-sided DVDs (a stack that's about 255.3 kilometers, or 158.65 miles, tall).
IU examples
Following are some examples of tera-, peta-, and exascale computing at IU:
- IU's Big Red 3 system has a theoretical peak performance (Rpeak) of 934 trillion floating-point operations per second (934 teraFLOPS).
- The SDA's tape library provides 79 PB of long-term storage capacity for research data.
- IU's Center for Research in Extreme Scale Technologies (CREST) develops methods, technologies, and training resources to enable exascale data analysis and computation.
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Last modified on 2020-11-24 15:59:31.