how to config when you have multiple GPUs ?

[Andrewp9]

hello everyone,

I have two GPU's. in the json for openCL I have:
"producerOpenCL": [
{
"platformIndex": 0,
"deviceType": -1,
"deviceIndex": 0,
...
I've played around with the parameters trying to get it to run on my second GPU without any luck. My preference would be to use both.
what configuration would I need to use both?

[daveshenal]

producerOpenCL is a JSON array. Eeach element is one device's own config (you can see this in your own snippet, since it opens with [). To use both GPUs, don't edit the existing entry... add a second object alongside it:

"producerOpenCL": [
  {
    "platformIndex": 0,
    "deviceType": -1,
    "deviceIndex": 0,
    ...
  },
  {
    "platformIndex": 0,
    "deviceType": -1,
    "deviceIndex": 1,
    ...
  }
]

Each entry runs as its own independent producer, so both GPUs work in parallel.

The likely reason your earlier attempts didn't work: deviceIndex is scoped per platform, not global across all GPUs in the system. If both your GPUs are the same vendor (e.g. two NVIDIA cards), they're almost always on the same platformIndex, so 0 and 1 is correct. But if they're different vendors (e.g. NVIDIA + an Intel iGPU), they're usually on two different platforms... so the second entry needs a different platformIndex (e.g. 1), not the same platformIndex with deviceIndex: 1. Guessing which combo is right without checking is exactly the kind of thing that silently fails.

To find the correct indices for your system: run clinfo -l (install clinfo if you don't have it) - it lists every OpenCL platform and the devices under it, in the same enumeration order the app uses. Match your second GPU's platform/device position from that output into your JSON. BitcoinAddressFinder's own startup log should also print the OpenCL devices it detects, which is worth checking too.

deviceType: -1 just means "any device type". leave that as is on both entries, it's not the issue.

[bernardladenthin]

The producerOpenCL field is a JSON array, so you can add one entry per GPU. Each entry targets a specific device via platformIndex + deviceIndex, and each must reference its own keyProducerId (key producers cannot be shared between producers).

Step 1 — find your platform/device indices

Run the tool with {"command": "OpenCLInfo"} first. It prints every OpenCL platform and device with their indices.

Step 2 — define one key producer per GPU

Each ProducerOpenCL needs its own dedicated key producer so both GPUs draw from independent random streams.

Step 3 — add a second entry to producerOpenCL

{
  "command": "Find",
  "finder": {
    "keyProducerJavaRandom": [
      {
        "keyProducerId": "gpu0_keys",
        "randomAlgorithm": "SECURE_RANDOM",
        "customSeed": 0,
        "privateKeyMaxNumBits": 256
      },
      {
        "keyProducerId": "gpu1_keys",
        "randomAlgorithm": "SECURE_RANDOM",
        "customSeed": 0,
        "privateKeyMaxNumBits": 256
      }
    ],
    "consumerJava": {
      "lmdbConfigurationReadOnly": {
        "lmdbDirectory": "lmdb",
        "useProxyOptimal": true,
        "logStatsOnInit": true,
        "logStatsOnClose": false,
        "addressLookupBackend": "LMDB_ONLY",
        "disableAddressLookup": false
      },
      "printStatisticsEveryNSeconds": 10,
      "threads": 8,
      "queuePollTimeoutMillis": 50,
      "queueSize": 8,
      "awaitQueueEmptySeconds": 60,
      "runtimePublicKeyCalculationCheck": false,
      "enableVanity": false,
      "vanityPattern": "1[Ee][Mm][Ii][Ll].*"
    },
    "awaitTerminateSeconds": 31536000000,
    "producerJava": [],
    "producerJavaSecretsFiles": [],
    "producerOpenCL": [
      {
        "platformIndex": 0,
        "deviceType": -1,
        "deviceIndex": 0,
        "maxResultReaderThreads": 4,
        "keyProducerId": "gpu0_keys",
        "batchSizeInBits": 18,
        "keysPerWorkItem": 16,
        "batchUsePrivateKeyIncrement": true,
        "logSecretBase": false,
        "runOnce": false,
        "shutdownTimeoutSeconds": 300,
        "enableGpuFilter": true,
        "transferAll": false,
        "useSafeGcdInverse": true
      },
      {
        "platformIndex": 0,
        "deviceType": -1,
        "deviceIndex": 1,
        "maxResultReaderThreads": 4,
        "keyProducerId": "gpu1_keys",
        "batchSizeInBits": 18,
        "keysPerWorkItem": 16,
        "batchUsePrivateKeyIncrement": true,
        "logSecretBase": false,
        "runOnce": false,
        "shutdownTimeoutSeconds": 300,
        "enableGpuFilter": true,
        "transferAll": false,
        "useSafeGcdInverse": true
      }
    ]
  }
}

Key points:

• deviceIndex: 0 and deviceIndex: 1 select the two GPUs. If your GPUs are on different OpenCI platforms (e.g. one NVIDIA, one AMD), you may need platformIndex: 0 for one and platformIndex: 1 for the other — OpenCLInfo will tell you.
• Each GPU entry needs its own keyProducerId — they must be unique across the whole config.
• queueSize was bumped to 8 to give two producers more buffer space; tune this based on your system's RAM.
• You can also mix CPU producers alongside the two GPU producers the same way config_Find_1OpenCLDeviceAnd2CPUProducer.json does.

[Andrewp9]

Yes, that works, thank you for your complete and detailed answer.

I now get org.jocl.CLException: CL_OUT_OF_RESOURCES I have about 50% ram available (of 96 gb) and large java memory available of Xmx32G. To troubleshoot I'm just running the one GPU (Intel Arc Pro Graphics) and it errors, but my nvidia RTX 500 is fine if I run it solo.

Any thoughts?

[bernardladenthin]

Hi @Andrewp9,

The CL_OUT_OF_RESOURCES on the Intel Arc Pro comes from the per-launch GPU buffer being too large for that device — lowering batchSizeInBits (to ~8) is the right workaround for now.

The next release adds a suggested start config: running the OpenCLInfo command will print recommended producerOpenCL.batchSizeInBits and keysPerWorkItem values derived from your device's reported capabilities (compute units and max memory allocation). With that, the Intel Arc Pro should get a sensible batch size out of the box instead of needing manual trial and error.

[Andrewp9]

Thank you. I will look forward to 1.6.2 !