Specifying plan classes in C++

If you need more generality than is provided by XML-based plan class specification, you can specify plan classes in C++.

The scheduler is linked with a function

bool app_plan(SCHEDULER_REQUEST &sreq, char* plan_class, HOST_USAGE&);

The sreq argument describes the host. It contains:

• in sreq.host field, a description of the host's hardware, including:
  • In p_vendor and p_model, the processor type
  • In p_features, the processor features (e.g., fpu tsc pae nx sse sse2 mmx)
  • In m_nbytes, the amount of RAM
• in sreq.coprocs, a list of the hosts's coprocessors.
• in core_client_version, the client's version number in MMmmRR form.

When called with a particular SCHEDULER_REQUEST and plan class, the function returns true if the host's resources are sufficient for apps of that class. If true, it populates the HOST_USAGE structure:

struct HOST_USAGE {
   double ncudas;     // number of NVIDIA GPUs used
   double natis;      // number of ATI GPUs used
   double gpu_ram;    // max amount of GPU RAM used
   double avg_ncpus;  // avg #CPUs used by app (may be fractional)
   double max_ncpus;  // max #CPUs used (not currently used for anything)
   double projected_flops;
      // an estimate of the actual FLOPS.
      // used to select versions, so make it higher for the preferred version
   double peak_flops;
      // the peak FLOPS of the devices to be used
   char cmdline[256]; // passed to the app as a cmdline argument;
                      // this can be used, e.g. to control the # of threads used
};

You can define your own set of plan classes, and link your own app_plan() function with the scheduler. The BOINC scheduler comes with a default app_plan() (in sched/sched_customize.cpp).

Example: a plan class for multithread apps

Here's a plan class function for a multicore app that it achieves a linear speedup on up to 64 processors, and no additional speedup beyond that.

bool app_plan_mt(
    SCHEDULER_REQUEST& sreq, HOST_USAGE& hu
) {
    double ncpus = g_wreq->effective_ncpus;
        // number of usable CPUs, taking user prefs into account
    int nthreads = (int)ncpus;
    if (nthreads > 64) nthreads = 64;
    hu.avg_ncpus = nthreads;
    hu.max_ncpus = nthreads;
    sprintf(hu.cmdline, "--nthreads %d", nthreads);
    hu.projected_flops = sreq.host.p_fpops*hu.avg_ncpus*.99;
        // the .99 ensures that on uniprocessors a sequential app
        // will be used in preferences to this
    hu.peak_flops = sreq.host.p_fpops*hu.avg_ncpus;
    return true;
}

Defining GPU plan classes

To define a new NVIDIA/CUDA plan class, add a new clause to app_plan_cuda() in sched/sched_customize.cpp. For example, the plan class cuda23 is defined by:

    ...
    if (!strcmp(plan_class, "cuda23")) {
        if (!cuda_check(c, hu,
            100,        // minimum compute capability (1.0)
            200,        // max compute capability (2.0)
            2030,       // min CUDA version (2.3)
            19500,      // min display driver version (195.00)
            384*MEGA,   // min video RAM
            1.,         // # of GPUs used (may be fractional, or an integer > 1)
            .01,        // fraction of FLOPS done by the CPU
            .21            // estimated GPU efficiency (actual/peak FLOPS)
        )) {
            return false;
        }
    }

To define a new ATI/CAL plan class, add a new clause to app_plan_ati(). For example:

    if (!strcmp(plan_class, "ati14")) {
        if (!ati_check(c, hu,
            1004000,    // min display driver version (10.4)
            false,      // require libraries named "ati", not "amd"
            384*MEGA,   // min video RAM
            1.,         // # of GPUs used (may be fractional, or an integer > 1)
            .01,        // fraction of FLOPS done by the CPU
            .21         // estimated GPU efficiency (actual/peak FLOPS)
        )) {
            return false;
        }
    }

To define a new OpenCL plan class, add a new clause to app_plan_opencl(). For example:

    if (!strcmp(plan_class, "opencl_nvidia_101")) {
        return opencl_check(
            c, hu,
            101,        // OpenCL version (1.1)
            256*MEGA,   // min video RAM
            1,          // # of GPUs used
            .1,         // fraction of FLOPS done by the CPU
            .21         // estimated GPU efficiency (actual/peak FLOPS)
        );
    }