putting MonetDB, CouchDB and MySQL in single line already shows seriousness of the question. First of all, TPC-H is decision support (ad-hoc, analytic) workload, and putting all data into memory needs comparison with in-memory ad-hoc platforms like MemSQL where the difference might not be as pronounced.
Also, it is easy to have hundreds of gigs of RAM for CPU driven systems, whereas GPU memory is still tiny. Yes, doing complex window functions on streaming data may seem fine, but anything requiring larger arenas of random data access would fall of the cliff.
For all the people talking about speeding your Wordpress, you need to look at OLTP or even more readonly small data benchmarks (TPC-C is already too complex). Database that is efficient at transactional workload has too much overhead for analytical processing.
Then there are all the datacenter considerations - getting rid of heat once you have thousands of GPUs around is no longer a trivial task, and may involve oil immersion or water cooling.
Yes, sorting a dataset can be faster, but assembling it from all the I/O devices and memory is a task that is the major expense. Thats why multithreading works - there are lots of waits for memory already, making them even longer would be difficult. The I/O we talk about is not just reading from disk or disks or ssds, it is also about getting into the chip, and bandwidth there is still constrained.
And yes, CPU can get quite busy in database server, for all the network, storage, compression, page and row mangling code has to run somewhere - sizing hardware for large scale databases is a tough balancing act. But very little of that can work on GPU in online world.
Nice research though:)
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putting MonetDB, CouchDB and MySQL in single line already shows seriousness of the question. First of all, TPC-H is decision support (ad-hoc, analytic) workload, and putting all data into memory needs comparison with in-memory ad-hoc platforms like MemSQL where the difference might not be as pronounced. Also, it is easy to have hundreds of gigs of RAM for CPU driven systems, whereas GPU memory is still tiny. Yes, doing complex window functions on streaming data may seem fine, but anything requiring larger arenas of random data access would fall of the cliff. For all the people talking about speeding your Wordpress, you need to look at OLTP or even more readonly small data benchmarks (TPC-C is already too complex). Database that is efficient at transactional workload has too much overhead for analytical processing. Then there are all the datacenter considerations - getting rid of heat once you have thousands of GPUs around is no longer a trivial task, and may involve oil immersion or water cooling. Yes, sorting a dataset can be faster, but assembling it from all the I/O devices and memory is a task that is the major expense. Thats why multithreading works - there are lots of waits for memory already, making them even longer would be difficult. The I/O we talk about is not just reading from disk or disks or ssds, it is also about getting into the chip, and bandwidth there is still constrained. And yes, CPU can get quite busy in database server, for all the network, storage, compression, page and row mangling code has to run somewhere - sizing hardware for large scale databases is a tough balancing act. But very little of that can work on GPU in online world. Nice research though :)