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Checkpointing is a technique to reduce the memory footprint of the tape in adjoint mode algorithmic differentiation. Instead of recording the full algorithm on tape, which can quickly result in gigabytes of memory in a large computation, the tape is recorded for specific stages of the algorithm, one at a time. This is illustrated in the following figure:


The algorithm is divided into stages, where the input data of each stage is stored in a checkpoint and the outputs are computed passively (without recording on tape). Once the final output of the algorithm is computed, the adjoint of the output is initialized and at each checkpoint during tape rollback:

  1. The inputs to the checkpoint are loaded,
  2. The operations of this stage only are recorded on tape,
  3. The output adjoints of this stage are initialized,
  4. The tape is rolled back for this stage, computing the adjoints of the stage inputs,
  5. The input adjoints are incremented by these values, and
  6. The tape is wiped before proceeding with the previous stage.

Using this method, the tape memory is limited by the amount needed to record one algorithm stage instead of the full algorithm. However, each forward computation is computed twice, hence checkpointing trades computation for memory.

In practice, as using less memory leads to higher cache-efficiency, checkpointing may be faster overall than recording the full algorithm even though more computations are performed.

Example Algorithm

To demonstrate the checkpointing method, we choose a simple repeated application of the sine function to a single input:

template <class T>
void repeated_sin(int n, T& x)
    for (int i = 0; i < n; ++i)
    x = sin(x);

We divide the for loop into equidistant stages and insert a checkpoint at each of these.

Checkpoint Callback

To create a checkpoint, we need to store the inputs of the stage and the slots in the tape for the inputs and outputs in a callback object inheriting from CheckpointCallback. The virtual method CheckpointCallback::computeAdjoint needs to be overridden to perform the per-stage adjoint computation. As all stages are identical, we choose to implement the functionality of all checkpoints within a single callback object and store the required inputs in a stack data structure. Alternatively we could have created a new checkpoint callback object at every checkpoint. The prototype for our callback is:

template <class Tape>
class SinCheckpointCallback : public xad::CheckpointCallback<Tape>
    typedef typename Tape::slot_type   slot_type;   // type for slot in the tape
    typedef typename Tape::value_type  value_type;  // double
    typedef typename Tape::active_type active_type; // AReal<double>

    active_type computeStage(int n, active_type& x); // forward computation
    void computeAdjoint(Tape* tape) override;        // adjoint computation

    std::stack<int> n_;                    // number of iterations in this stage
    std::stack<value_type> x_;             // input values for this stage
    std::stack<slot_type> slots_;          // tape slots for input and output

For convenience of implementation, we added the forward computation for one stage within the same class in the computeStage method, which could also be performed outside of the object.

computeStage Method

Within the computeStage method, we first store the input value, the number of iterations, and the slots of the input in the checkpoint object:

value_type x_p = value(x);

We then compute the stage with the passive variable (not recording on the tape):

repeated_sin(n, x_p);

The value of the output active variable needs to be updated with the result and we need to store the slot of the output variable in the checkpoint also:

value(x) = x_p;

Note that we did not need to register x as an output with the tape here, as we had to do with the external functions example before, since the variable is already registered on tape (it's both input and output).

What is left is to register this callback object with the tape so that its computeAdjoint method is called at this point when the tape is rolled back:


computeAdjoint Method

The computeAdjoint method is called automatically by XAD at the checkpoints in the tape. We first need to load the inputs to this computation stage and obtain the adjoint of the output:

slot_type outputidx =;  slots_.pop();
slot_type inputidx =;   slots_.pop();
int n =;                    n_.pop();
value_type outputadj = tape->getAndResetOutputAdjoint(outputslot);

The function Tape::getAndResetOutputAdjoint reads the adjoint corresponding to the slot given and resets it to 0. This reset is generally required as the variable corresponding to the slot may be re-used (overwritten) in the algorithm, as is the case in the repeated_sin function.

We now want to use XAD to compute the adjoints just for this computation stage. This is performed by creating a nested recording within the global tape, than can be rolled back individually:

active_type x =;               // local independent variable
tape->registerInput(x);                 // need to register to record

xad::ScopedNestedRecording<Tape> nested(tape);  // nested recording
repeated_sin(n, x_ad);                  // run actively
tape->registerOutput(x);                // register x as an output
derivative(x) = output_adj;             // set output adjoint
nested.computeAdjoints();               // rollback nested tape

nested.incrementAdjoint(inputslot, derivative(x));  // incr. input adjoint

In a similar fashion to simple adjoint mode (see Basic Usage), we first initialize the local independent variables as active data types and start a nested recording. This is performed by creating a local object nested of type ScopedNestedRecording, which wraps calls to Tape::newNestedRecording in its constructor and Tape::endNestedRecording in its destructor. It is recommended to use the ScopedNestedRecording for this purpose to make sure the nested recording is always finished when the scope is left.

Next we record the operations for this stage by running the algorithm actively. We then set the adjoint of the output and compute the adjoints of the inputs. The adjoints of the inputs to this stage can then be incremented.

Note that when the nested object goes out of scope, i.e. when its destructor is called, the nested tape for this computation stage is wiped and the memory can be reused for the previous stage. This saves overall memory.


The full algorithm with checkpointing can then be initiated as follows:

tape_type tape;

AD x_ad = x;                             // initialized indepedent variables
tape.registerInput(x_ad);                // register with the tape
tape.newRecording();                     // start recording derivatives

SinCheckpointCallback<tape_type> chkpt;  // setup checkpointing object

int checkpt_distance = 4;                // we checkpoint every 4 iterations
for (int i = 0; i < n; i += checkpt_distance)
    int m = min(checkpt_distance, n-i);
    chkpt.computeStage(m, x_ad);             // one computation stage

derivative(x_ad) = 1.0;

std::cout << "xout       = " << value(x_ad) << "\n"
          << "dxout/dxin = " << derivative(x_ad) << "\n";

This follows largely the same procedure as given in Basic Usage, but setting up the checkpoint object and calling its computeStage member for every stage of the algorithm (4 iterations in this example).


It is important that the checkpoint callback object is valid when Tape::computeAdjoints is called. It should not be destroyed before.

See Checkpoint Callback Memory Management for how to use tape-based destruction with dynamically allocated checkpoint callbacks.

See also

This example is included with XAD (checkpointing).

Other Usage Patterns

Alternative methods may be used to update the adjoints within a checkpoint's CheckpointCallback::computeAdjoint method, such as:

  • Forward mode algorithmic differentiation within an outer adjoint mode
  • Finite differences (bumping)
  • Analytic derivatives
  • External library functions (see External Functions)

Checkpointing can also be used recursively, i.e., new checkpoints are created within a nested tape in a checkpoint.

The benefits of each of these approaches are highly application-dependent.