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Doesn't the overhead of moving things back and forth between GPU memory and main memory wipe out most potential gains, though? If you're running analytical wor

by jbooth 10y ago

Doesn't the overhead of moving things back and forth between GPU memory and main memory wipe out most potential gains, though? If you're running analytical workloads on big data sets, you're typically I/O bound to start with. It seems like managing moving little pieces of it back and forth to the GPU to compute is going to be a big PITA, add lots of little latencies, and gain you absolutely nothing. What am I missing there?

felipe_aramburu 10y ago
At Blazing we also build GPU db and have always loved what this project (Alenka) is doing. First of all when you are talking about I/O bound which I/O are you talking about? Do you mean from disk? From RAM? There are many ways of getting around some of these I/O bottleknecks like sending compressed data or processing while transferring. You're assumption that these workloads are typically I/O bound is correct but then agian GPU databases aren't always going after the most "typical" workloads. If you are doing large amounts of transformations, or complicated joins then you also can benifite hugely from the use of a gpu. Ever try to join several tables together across multiple columns? If you do then you should probably use a hash join and if you are using a hash join you better believe you are going to want to do be doing computationally intensive things like sorting and hash generation. Have you tried any gpu databases to see if this concern is valid? GPU dbs can take advantage of things like very expensive cascading compression that many normal databases can't.
arnon 10y ago
1. Not everything needs to be pushed up to the GPU. Some things are better left in RAM. 2. What if you only push indexes or similar up to the GPU, like an AB-tree index? You're keeping all of the 'heavy' stuff down, and only uploading a representation of it, to be later replaced with the actual data. 3. Think compression/decompression done on the GPU directly.

by jbooth 10y ago

felipe_aramburu 10y ago

At Blazing we also build GPU db and have always loved what this project (Alenka) is doing. First of all when you are talking about I/O bound which I/O are you talking about? Do you mean from disk? From RAM? There are many ways of getting around some of these I/O bottleknecks like sending compressed data or processing while transferring. You're assumption that these workloads are typically I/O bound is correct but then agian GPU databases aren't always going after the most "typical" workloads. If you are doing large amounts of transformations, or complicated joins then you also can benifite hugely from the use of a gpu. Ever try to join several tables together across multiple columns? If you do then you should probably use a hash join and if you are using a hash join you better believe you are going to want to do be doing computationally intensive things like sorting and hash generation. Have you tried any gpu databases to see if this concern is valid? GPU dbs can take advantage of things like very expensive cascading compression that many normal databases can't.

arnon 10y ago

1. Not everything needs to be pushed up to the GPU. Some things are better left in RAM. 2. What if you only push indexes or similar up to the GPU, like an AB-tree index? You're keeping all of the 'heavy' stuff down, and only uploading a representation of it, to be later replaced with the actual data. 3. Think compression/decompression done on the GPU directly.