3 ms·

HP announced their ambition to do that a few years ago, framing it as a grand vision of the future of computing. I haven't heard anything since. This kind of of

HP announced their ambition to do that a few years ago, framing it as a grand vision of the future of computing. I haven't heard anything since. This kind of offloading or distributed computing isn't quite a new idea, but it hasn't materialized yet. I suspect that it is too tough a nut to crack for the general case.

inetknght 8y ago
My understanding is that GPUs have moved toward what I described: thousands of discrete cores with large amounts of math performance but generally terrible (or even non-existent) branching...
- gmueckl 8y ago
  And transferring data to and from the GPU is also a source of overhead.
  - inetknght 8y ago
    Despite having an overhead of transferring data to and from the GPU: it's still faster to move the data to the GPU and let the GPU process it locally than it is to leave it in main RAM away from the processing. Transferring data to and from the CPU is also a source of overhead; even your CPU has cache to bring the data more local. So what's your point?
    - gmueckl 8y ago
      Whatnyounsay is not true as a blanket statement: the overhead for transferring data to GPU memory is higher than accessing main memory from the CPU, so depending on what you do, the data transfer to the GPU might not result in a performance gain at all.
- deRerum 8y ago
  GPUs are latency hiding engines...they address the mismatch between processor clock speeds and memory latency by a unique scheme. Since they can’t improve memory latency, instead they parallelize memory and vastly increase the bandwidth available. Once they have increased memory system bandwidth to be able to feed the multiprocessor throughput, the rest of the architecture is designed to make the most efficient use of it. They spawn thousands of threads and schedule them in and out really quickly... so the processor utilization is always as close to 100% as possible. When a thread is waiting for memory it is put to sleep in a few clock cycles. When it’s data is available it wakes up and does it’s business. Since the workload is split among thousands of threads therefore there is somebody ready to be scheduled. This is why GPUs only make sense to run on massive workloads. The same trick can be used to hide SSD latency.

inetknght 8y ago

My understanding is that GPUs have moved toward what I described: thousands of discrete cores with large amounts of math performance but generally terrible (or even non-existent) branching...

gmueckl 8y ago
And transferring data to and from the GPU is also a source of overhead.
- inetknght 8y ago
  Despite having an overhead of transferring data to and from the GPU: it's still faster to move the data to the GPU and let the GPU process it locally than it is to leave it in main RAM away from the processing. Transferring data to and from the CPU is also a source of overhead; even your CPU has cache to bring the data more local. So what's your point?
  - gmueckl 8y ago
    Whatnyounsay is not true as a blanket statement: the overhead for transferring data to GPU memory is higher than accessing main memory from the CPU, so depending on what you do, the data transfer to the GPU might not result in a performance gain at all.
deRerum 8y ago
GPUs are latency hiding engines...they address the mismatch between processor clock speeds and memory latency by a unique scheme. Since they can’t improve memory latency, instead they parallelize memory and vastly increase the bandwidth available. Once they have increased memory system bandwidth to be able to feed the multiprocessor throughput, the rest of the architecture is designed to make the most efficient use of it. They spawn thousands of threads and schedule them in and out really quickly... so the processor utilization is always as close to 100% as possible. When a thread is waiting for memory it is put to sleep in a few clock cycles. When it’s data is available it wakes up and does it’s business. Since the workload is split among thousands of threads therefore there is somebody ready to be scheduled. This is why GPUs only make sense to run on massive workloads. The same trick can be used to hide SSD latency.