5 ms·

Why are they optimized for vectorized floating-point multipliers? Does the CEO of Intel just tell all the engineers to do this because he likes multiplication?

daveguy 10y ago
They are optimized for that because a lot of algorithms can make use of them, from quicksort/mergesort through image rendering and encryption. It is an easy optimization from a hardware perspective -- simple repetitive hardware structure. This is why GPUs are so powerful and games are not the only thing that benefits from this type of optimization. Matrix multiplication is also used in signal processing. The CEO asked, how can we optimize the use of our hardware for the most benefit? And SIMD with wide pipes is at the top of the list. Most of the post is about all the new algorithms that can take advantage of the hardware push. The hardware push is there because it is an easy use of hardware resources. This is also an optimization that compilers can readily take advantage of on a small scale (similar to pipelining) so the combination of benefit + ability to use + simplicity/low resource use makes it an inevitability.
- mcguire 10y ago
  A sort that uses vectorized floating point multiplication?
  - sbierwagen 10y ago
    From 2008: http://www.vldb.org/pvldb/1/1454171.pdf http://www.vldb.org/pvldb/1/1454171.pdf Also: http://researcher.watson.ibm.com/researcher/files/jp-INOUEHRS/PACT2007-SIMDsort.pdf http://researcher.watson.ibm.com/researcher/files/jp-INOUEHR... and https://github.com/NumScale/boost.simd https://github.com/NumScale/boost.simd
sapphireblue 10y ago
Because CPUs are designed around benchmarks representative of real workloads people are running on their computers. Naturally, multimedia and games are a large part of these workloads, this is what drives SIMD adoption.
pavanky 10y ago
No, it's done because it covers two of their largest markets Gaming machines and clusters used for scientific / finance applications.

daveguy 10y ago

They are optimized for that because a lot of algorithms can make use of them, from quicksort/mergesort through image rendering and encryption. It is an easy optimization from a hardware perspective -- simple repetitive hardware structure. This is why GPUs are so powerful and games are not the only thing that benefits from this type of optimization. Matrix multiplication is also used in signal processing. The CEO asked, how can we optimize the use of our hardware for the most benefit? And SIMD with wide pipes is at the top of the list. Most of the post is about all the new algorithms that can take advantage of the hardware push. The hardware push is there because it is an easy use of hardware resources. This is also an optimization that compilers can readily take advantage of on a small scale (similar to pipelining) so the combination of benefit + ability to use + simplicity/low resource use makes it an inevitability.

mcguire 10y ago
A sort that uses vectorized floating point multiplication?
- sbierwagen 10y ago
  From 2008: http://www.vldb.org/pvldb/1/1454171.pdf http://www.vldb.org/pvldb/1/1454171.pdf Also: http://researcher.watson.ibm.com/researcher/files/jp-INOUEHRS/PACT2007-SIMDsort.pdf http://researcher.watson.ibm.com/researcher/files/jp-INOUEHR... and https://github.com/NumScale/boost.simd https://github.com/NumScale/boost.simd

sapphireblue 10y ago

Because CPUs are designed around benchmarks representative of real workloads people are running on their computers. Naturally, multimedia and games are a large part of these workloads, this is what drives SIMD adoption.

pavanky 10y ago

No, it's done because it covers two of their largest markets Gaming machines and clusters used for scientific / finance applications.