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+cudadl-0.9
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+==========
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+
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+21 Mar 2012
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+Ryan Henry and Ian Goldberg
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+{rhenry,iang}@cs.uwaterloo.ca
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+http://crysp.uwaterloo.ca/software/
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+
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+This package contains the source code to our CUDA implementation of
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+van Oorschot and Wiener's parallel version of the Pollard rho discrete
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+log algorithm. It is intended for use on 1536-bit moduli that are
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+RSA numbers with smooth totient; that is, the modulus N=pq, where p and q
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+are 768-bit primes, and the prime factors of p-1 and q-1 are all
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+distinct and less then B, for a parameter B. [The value 1536 is
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+hardcoded as "WORDS = 24" (24\*32\*2 = 1536) in the Makefile; it is easy
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+to change this value and recompile if desired.] Note that this means
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+the totient of N = \phi(N) = (p-1)(q-1) has all prime factors less than
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+B; that is, \phi(n) is "B-smooth".
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+
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+There are two versions of this program: a 2-GPU version and a multi-GPU
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+distributed version. The first uses only 2 GPUs on a single computer, but
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+is much simpler and doesn't require Libevent or MPI. The second version
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+can run across many computers and GPUs.
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+
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+This software is described in "Solving Discrete Logarithms in
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+Smooth-Order Groups with CUDA", CACR technical report 2012-02,
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+http://www.cacr.math.uwaterloo.ca/techreports/2012/cacr2012-02.pdf
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+
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+This program is covered under version 3 of the GNU General Public
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+Licence; see the file COPYING for more information.
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+
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+Building
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+--------
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+
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+ 1. Download and install:
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+ * NTL
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+ * GMP
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+ * NVIDIA CUDA Toolkit 9.0
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+
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+ 2. If you wish to run the distributed version, you will also need:
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+ * Libevent
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+ * OpenMPI
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+
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+ 3. Make a new `Makefile.local` file. An example:
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+
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+ MACROS = -DVERBOSE
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+ CUDA_ARCH ?= sm_60
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+
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+ GMP ?=
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+ CUDA ?=
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+ MPI ?= /usr/local
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+
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+ Most variables have default values. If you set any of the library paths
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+ to an empty value, they will not be set to the default value. This is
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+ useful on systems like SHARCNET which loads libraries automatically, and
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+ where you should not choose any path.
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+
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+ 4. To build the 2-GPU version, run `make nompi`. To build the distributed
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+ version, run `make all`.
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+
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+Requirements
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+------------
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+
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+ 1. Two compute capability level 3.0 CUDA cards to run the 2-GPU version (if
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+ you have more or just 1, you'll need to modify dlrho.cc, unfortunately).
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+ If running the distributed version, you can use any number of GPUs.
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+
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+ 2. If running the MPI version, passwordless ssh is highly recommended. Make
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+ sure to also ssh from every node to every other node in order to accept
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+ the host key.
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+
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+Usage
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+-----
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+
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+ 1. Create the modulus N as, for example, a 1536-bit RSA number whose
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+ totient is 2^50-smooth:
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+
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+ ./gen_N 1536 50 > N
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+
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+ 2. Generate a DL problem mod N and solve it:
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+
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+ 1. 2-GPU version:
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+
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+ ./dlrho < N
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+
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+ 2. Distributed version:
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+
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+ mpirun --bind-to none --mca plm_rsh_no_tree_spawn 1 -n 4 --host grunt3,grunt4,grunt5,grunt6 bash -c "./mpi 8 1 \$HOME/data -w 32 -n 4 -m 100 N 1"
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+
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+ The `--bind-to none` is important to prevent binding to CPU cores, which doesn't play nicely with cudadl which forks.
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+
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+ 3. Distributed version with Slurm (not using MPI; you must be running on a network file system):
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+
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+ Update the job parameters in `slurm-jobscript.sh`.
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+
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+ sbatch slurm-jobscript.sh N data_output_dir cudadl_path
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+
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+Macros
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+------
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+
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+ * VERBOSE: Print extra debugging information.
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+
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+ * DERANDOMIZE (`f627a022`): Deterministically generate the same problem for
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+ a given N file. Will choose a deterministic problem (base and target),
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+ and Pollard's rho parameters (a, b, astep, and bstep). The
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+ distinguished points will also be arranged deterministically after each
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+ kernel launch.
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+
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+ * SAVE\_DPS (`1da839f0`): Save the distinguished points to a file. By setting
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+ the SAVE_DPS macro, you can save the distinguished points. The points
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+ discovered during each subproblem are saved in individual files (or
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+ multiple files in the case of multiple dpnodes). The file name format
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+ is 'dplist_{subproblemid}.out' for the dlrho version, or
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+ 'dplist_{dpnodeid}_{subproblemid}.out' for the controller/worker
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+ version. This can generate a large amount of data, so it's best to use
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+ it on small problems (B < 2^60). When run with the DERANDOMIZE macro,
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+ it should produce the same points each time, except in the case of
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+ small subproblems where dpfreq is large and the device dp buffer is
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+ filled. In this case the points will not be the same, and this can be
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+ detected by the line "Warning: The device dp buffer was filled, so some
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+ points were not recorded." in the worker's stderr.
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+
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+ * MAKE\_VERSIONS\_COMPARABLE (`f496ab2b`): Make the output from the two
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+ versions (dlrho and controller/worker) comparable. This forces the
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+ controller/worker version to use one worker per subproblem and one
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+ dpnode per subproblem. This should make the saved distinguished points
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+ comparable between the two, but the number of kernel launches will be
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+ slightly higher in the controller/worker version since the worker will
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+ usually run an extra kernel launch before it receives the stop message
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+ from the dpnode.
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+
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+ * RIPPLE (`d8d46482`): Add the suffix '-data0' to the hostname in order to
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+ use the 4*40Gbps bonded interface on the University of Waterloo's
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+ RIPPLE facility.
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+
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+ * GRAHAM (`d8d46482`): Add the prefix 'ic-' to the hostname in order to use
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+ the Infinband interface on SHARCNET's Graham cluster.
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+
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+ * DPFREQ\_DIVISOR (`563cc16a`): Reduce the dp frequency by this number. This
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+ is useful when the desired resources calculations provide a dpfreq that
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+ is too large and fills the device dp buffers.
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+
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+Tips and Example Makefiles
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+--------------------------
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+
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+For these example makefiles, copy them into a `Makefile.local` file.
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+
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+ * RIPPLE: The NTL package installed on RIPPLE is too old for the current
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+ version of cudadl, so you must compile a newer version manually. The
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+ number of threads/blocks in the makefile below work well, but could
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+ be optimized further.
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+
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+ MACROS = -DRIPPLE -DNTHREADS=16384 -DNBLOCKS=32
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+ CUDA_ARCH = sm_30
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+
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+ NTL ?= <fill this in>
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+
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+ * Graham: The Graham module system will automatically load the CUDA and GMP
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+ packages. You will need to build NTL and Libevent manually. You cannot
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+ run the MPI version on Graham since the MPI version forks (which isn't
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+ compatible with Infiniband). Instead, you must use the non-MPI version
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+ designed for Slurm. The number of threads/blocks in the makefile below
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+ work well, but could be optimized further.
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+
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+ Before running make, run `module load cuda/9.0.176`.
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+
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+ MACROS = -DGRAHAM -DNTHREADS=71680 -DNBLOCKS=112 -DDPFREQ_DIVISOR=4
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+ CUDA_ARCH = sm_60
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+
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+ GMP ?= <leave this blank>
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+ NTL ?= <fill this in>
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+ CUDA ?= <leave this blank>
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+ LIBEVENT ?= <fill this in>
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+ MPI ?= <leave this blank>
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+
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+
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+ * Cedar: The Cedar module system will automatically load the CUDA and GMP
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+ packages. You will need to build NTL and Libevent manually. You cannot
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+ run the MPI version on Cedar since the MPI version forks (which isn't
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+ compatible with Infiniband). Instead, you must use the non-MPI version
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+ designed for Slurm. The number of threads/blocks in the makefile below
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+ work well, but could be optimized further.
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+
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+ Before running make, run `module load cuda/9.0.176`.
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+
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+ MACROS = -DNTHREADS=71680 -DNBLOCKS=112 -DDPFREQ_DIVISOR=4
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+ CUDA_ARCH = sm_60
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+
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+ GMP ?= <leave this blank>
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+ NTL ?= <fill this in>
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+ CUDA ?= <leave this blank>
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+ LIBEVENT ?= <fill this in>
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+ MPI ?= <leave this blank>
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+
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+Changelog
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+---------
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+
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+ * 0.9 (21 Mar 2012)
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+
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+ Extend the code to handle smoothness levels (B) larger than 2^60. Now
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+ we can handle up to 2^92. We have successfully run a test with
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+ B = 2^80.
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+
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+ * 0.8 (23 Jan 2012)
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+
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+ Initial public release
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