Portable and Flexible DGEMM Library for GPUs (OpenCL, CUDA, CAL) with special support for HPL
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README

////////////////////////////////////////////////////////////////////////////////////////////////////////////////
Caldgemm Readme, Command Line Options, Performance Optimization Guide, and examples
////////////////////////////////////////////////////////////////////////////////////////////////////////////////

Command Line Options of dgemm_bench:
The parameters here are those of DGEMM bench, and the defaults are valid for DGEMM bench.
Most parameters translate directly to a CALDGEMM setting, in that case the relevant
CALDGEMM setting with its default in CALDGEMM is listed.

Some CALDGEMM settings are only valid for HPL-GPU. In that case, there is usually still
a DGEMM bench option, to test this parameter. These parameters are marked (HPL-GPU Setting).

CALDGEMM provides 4 backends, for CAL, OpenCL, CUDA, and CPU. Some parameters are only
valid for only one or some backends. This is noted as e.g. (CAL Runtime and OpenCL Runtime
only).

CALDGEMM has two DMA Frameworks, one keeps the C matrix on GPU (GPU_C = 1), on keeps
the C matrix on the host (GPU_C = 0). This is switched with -Oc switch. Some parameters
are only valid for the either or the other case. This is noted as e.g. (GPU_C = 1 only).
The CAL Runtime will always use GPU_C = 0, CUDA will always use GPU_C = 1, OpenCL supports
both, for CPU backend this setting is ignored. In general, GPU_C = 1 should be favored
when the GPU is much faster as CPU (i.e. with a multi-gpu system), GPU_C = 0 is better
when GPU and CPU performance do not differ by more than a factor 4. The CPU_C = 0 option
requires preprocessing (DivideBuffer) and postprocessing (MergeBuffer) on the host.
Compared to GPU_C = 0, GPU_C = 1 requires half the global host memory bandwidth, but it
requires full duplex DMA transfer instead of half duplex for GPU_C = 0.

-?
Display help on command line options.

-e (default: disabled)
Verify Computational Correctness. The matrix is copied at the beginning of the computation.
Sufficient memory must be available. See -7 for verification of large matrices.

-q (default: disabled)
Supress Display Output in caldgemm. Output from dgemm_bench is still active.
See -5 to suppress this.

-a (default: disabled) (CAL Runtime Only)
Print the disassembled kernel image

-i (default: disabled) (CAL Runtime and OpenCL Runtime Only)
Print IL Kernel used

-if <int> (default -1 = autodetect)
Force DGEMM Kernel Variant to use. CALDGEMM can use some special kernels for special cases,
i.e. general (number 0), with beta=1 (number 1), with beta = 1 and alpha = 0 and hardcoded k
(number 2), with beta = -1 and alpha = 1 (number 4). CALDGEMM will automatically use the
correct kernel.
Used for internal testing only.

-o <c|g> (default: 'c')
Specify the output location, c = CPU, g = GPU, default GPU.
This is the output location of the kernel. If 'g' is specified the GPU write to GPU global
memory and an additional DMA transfer fetches the data to the host. In general 'c' is the
faster option. On some systems DMA is slow and 'g' gets the better kernel performance.
See -I in combination with the 'g' option!

-I (default: -1 = autodetect) (CAL Runtime Only)
Force implicit driver sync.
A bug in some AMD drivers prohibits DMA transfers and concurrent kernel execution in certain
situations. Ths slows down caldgemm. A workaround is
available that relies on a specific driver behavior and might result in wrong results with
newer drivers. It is automatically detected whether you driver suffers by the bug and whether
the workaround can be applied. This check does not work for newer driver versions though.
-I forces the workaround enabled.

-^  <int> (CAL Runtime Only)
Set DMA fetch queue parameter. Some AMD GPU drivers show a bug with implicit driver sync,
but they still prohibits the concurrent DMA transfer (see -I). In this case, the implicit
driver sync (-I) cannot be used, but must be switched off. This would disallow concurrent
DMA transfers. A DMA fetch queue is a second workaround, which works in general, but is
slower than the implicit driver sync. In general, if the driver does not show the DMA
limitation, no workaround should be used (-I 0 -^ 0), if the driver has the limitation and
implicit driver sync does not cause data corruption, the implicit driver sync workaround
should be used (-I 1 -^ 0), if implicit driver sync does not work, the DMA fetch queue
should be used (-I 0 -^ 1).

-h <int> (default: =4096)
Tile size for matrix multiply, default 4096. If you use GPU only DGEMM the matrix sizes must
be a multiple of h.

-H <int> (default: =-h)
Reduced block size for actual matrix multiply (buffer size given by -h).
I.e. CALDGEMM will allocate buffers for a tile size of h, but then use an actual tile size of
H. The reason is that when you want to run caldgemm with different matrix sizes, you should
initialize it with large h suited for the largest matrix, but a smaller matrix might favor a
smaller h, so tile size can be reduced during runtime in caldgemm. H is used to test the
impact of this in dgemm_bench.
Used for internal testing.

-w <int> (default: 1024)
k for matrix multiply, default 1024.

-W <int> (default: =-w)
Reduced width, see H.
Used for internal testing.

-l (default: disabled)
Automatically select tile-size for good performance. The -h paramameter defines the maximal
size possible. -l parameter will use smaller tiles for smaller matrices. Activating this is
generally a good idea.

-m <int> (default: 4096)
m for matrix multiply, default 1024.
Number of rows of the target matrix. If GPU-only DGEMM is used, this must be a multiple of
-H. If small tiles are allowed via -J switch, it must be a multiple of the minimum small
tile size. If CPU us used as well, m can be arbitrary. The CPU processes the remainder part.

-n <int> (default: 4096)
n for matrix multiply, must be multiple of h, default 1024.
Number of cols of the target matrix. If GPU-only DGEMM is used this must be a multiple of -H.

-v (default: disabled)
Verbose Synchronous Timing for Single Kernels / Transfers.
This disables all asynchronous transfers in caldgemm. Overall performance will be poor.
This can be used for directly measuring kernel performance and DMA performance and pre-/
postprocessing performance on CPU (pre-/postprocessing is only used for some operating modes.)

-k (default: disabled) (GPU_C = 0 Only)
Print Timing of Asynchronous DGEMM Operation.
Used for internal testing.

-r <int> (default: 1)
Number of iterations to run the program (inside caldgemm)
Used for internal testing.

-R <int> (default: 1)
Number of iterations to run in the benchmark (seperate caldgemm calls)
Used for internal testing.

-y <int> (default: -1)
Force Device ID (-1 = all devices)
Force the device id to use. You can either specify a single device or provide -1 to use all
devices.

-Y <int> (default: 8)
Maximal number of devices to use. Setting -Y greater than zero requires -y to be -1

-bb <int> (default: 0 = autodetection)
Maxumum number of allowed bbuffers. In many cases, mostly for OpenCL autodetection might not
work properly. Then, -bb should be set to the higherst max(m,n)/h which is run.

-d (default: disabled)
Print lots of debug output

-z (default: disabled)
Enable Multithreading. You definitely want to activate this. For some internal reasons, this
is a prerequisite to use multiple GPUs. MultiThreading means asynchronous processing of pre-/
postprocessing (required if GPU_C = 1 (-Oc parameter)). In addition, it is required for
asynchronous factorization, broadcase, etc. in HPL-GPU.

-Z (default: disabled)
Enabld Multithreading for DivideBuffer as well. Requires -z. Only valid for multiple GPUs. Use
-Gx to set CPUs for GPU pre/postprocessing!.

-b (default: disabled)
Enable internal benchmarking mode.
Used for internal testing.

-c (default: disabled)
Use CPU for DGEMM. You can supply -g as well to use both CPU and GPU. Supplying neither of them
will use GPU only.

-g (default: enabled if and only if -c is disabled)
Use GPU for DGEMM. You can supply -g as well to use both CPU and GPU. Supplying neither of them
will use GPU only.

-f (default: disabled)
Fast Init (Empty Matrices). The matrices are filled with zeros instead of using a random number
generator. Initialization is faster. Use for optimization and benchmarking only. The verification
does not work with this initialization method. Neither are the benchmark results correct with
newer GPUs. Multiplication with zeroes drains less power, hence the GPU will run in turbo mode
constantly, which is not true for standard random numbers.

-j <dbl> (default: -1)
Ratio of GPU performance to total CPU+GPU performance. Set to -1 for autodetection. This defines
how the matrix is split between CPU and GPU. The GPU will process a fraction of j. For DGEMM only,
this should be the GPU_Perf/(CPU_Perf + GPU_Perf). When used within HPL-GPU, keep in mind that the
CPU will have to perform other tasks as well, so j should be larger then. The -1 autodetection is
usually missing a good initial guess for the first run, i.e. it will find a good value over time but
at the beginning it can be far of. You can use a negative value to define an initial guess, i.e.
-j -0.7 will start with a ratio of 0.7 and then refine this automatically.

-jf <dbl> (default: disabled) (HPL-GPU Setting for Multi-Node runs)
If greater zero, this defines a minimum gpu ratio that is used during factorization phases of linpaxk.
In these phases, the factorization causes significant cpu load. The ratio should thus be higher than
in non-factorization phases. In that case -jf defines a lower limit to support the autocalculation.

-jm <dbl> (default: disabled)
If greater zero, this defines a maximum gpu ratio. This ensures the CPU always gets a certain part of
the matrix. This is particularly usefull in combination with automatic ratio calulation. Autocalculation
works only if the CPU has a certain part. Without -jm, as soon as the CPU part became 0 once, it will
usually remain zero and never recover. In that case, you can use -jm 0.99.

-jt <bdbl> (default: 0)
The automatic ratio calculation tries to match gpu and cpu execution time to ensure 100% execution of
both processors. Performance is deterioted more, if the GPU idles, hence it is generally a good idea
to aim for slightly longer gpu execution time than cpu execution time, to compensate for small
variations. This margin parameter defines a margin in seconds, which defines how much GPU time should
exceed CPU time.

-js <dbl> (default: 0.4) (HPL-GPU Setting)
In linpack factorization phases, execution time variations can be larger. This setting overrides the
-jt setting in this case.

-jl <dbl> (default: 0.2) (HPL-GPU Setting)
With the standard (non-alternate) lookahead, the CPU has to process a small non-quadratic matrix-part
in the preparatory phase of lookahead. DGEMM on this part is usually slower than the full DGEMM. This
parameter defines an extra factor that virtually increases the lookahead part in the CPU / GP
distribution calculation, to account for reduces CPU performance. (a setting of zero means no
compensation.)

-jp <int> (default: 1)
Apply ratio penalties to CPU part in some situations to ensure GPU keeps the dominant processor. A
setting of 0 disables penalties. Setting of 1 applies a penalty if the CPU took longer than the GPU in
the last iteration. A setting of two applies a penalty in addition when the CPU part in the last
iteration was short, because in that case CPU performance may fluctuate and is not that important.

-jq <dbl> (default: 0.9)
Penalty factor to apply to CPU part.

-s (default: disabled)
Dynamic CPU / GPU scheduling. Do not use only the fixed ratio specified by -j but use a dynamic CPU/GPU
workload scheduling. This includes work-stealing, etc. The value provided by -j is the basis for the
scheduling.

-M (default: disabled)
Disable third phase in dynamic scheduling

-N (default: disabled)
Disable second phase in dynamic scheduling

-rr (default: disabled) (HPL-GPU Setting)
Rereserve Linpack CPU: HPL-GPU requires one GPU core for the broadcast. This core is not available for
CPU DGEMM. CALDGEMM can estimate the broadcast time and then try split the DGEMM in two parts. One part
in parallel to the broadcast with one core less, and then a second part after the broadcast with all
cores. Makes sense when you are not GPU dominated and when you do not have too many cpu cores.

-p (default: disabled)
Interleaving Memory Policy. Gotoblas usually activates memory interleaving. This leads to a problem with
the CAL library. Interleaving should be activated after memory for the CAL library is allocated. Thus
it is recommended to disable interleaving in GotoBLAS (apply the patch provided with caldgemm and
set NO_MEMINTERLEAVE in GotoBLAS Make.rule) and use -p.

-u (default: disabled)
Dump Test Matrix.
Used for internal testing only.

-1 (default: disabled)
Transpose A Matrix. Provide a transposed input A matrix.

-2 (default: disabled)
Transpose B Matrix. Provide a transposed input B matrix.

-3 (default: disabled)
Set alpha parameter to 1.0 to test optimized kernel.

-# (default: disabled)
Set beta parameter to 0.0 to test optimized memcpy.

-5 (default: disabled)
Quiet Benchmark mode (different from quiet caldgemm mode -q). This suppresses output of dgemm_bench.
Output of caldgemm is not suppressed. See -q for this.

-6 <int> (default: not used)
Set m=n = value * tile-size (-h)

-4 <int> (default: not used)
Set m=n to the closest multiple of tile-size (-h) to value

-7 (default: disabled)
Verfication for large matrices. Compared to -e this does not require the matrix to be copied. However,
the output is less elaborated and it only tells you whether the DGEMM succeeded.

-8 (default: initial run enabled)
No initial run to negate cache effects. The first run is usually slower as the kernel must be copied
to GPU, etc. Thus, for benchmarks, an initial run is performed before the actual benchmark run is
started. The -8 option ommits this initial run. The initial run is automatically deactivated if the
-d option or some other are given. This option is primarily used for debugging.

-9 (default: disabled)
Output a table with timing information

-0 (default: disabled) (CAL Runtime only)
Write the output of divideBuffers-function directly to GPU instead of a seperate DMA transfer. This
option turned out to not perform well. Better leave it deactivated.

-A (default: disabled)
Do the DMA transfer to GPU asynchronously. If you are not debugging, always enable this.

-L (default: disabled)
Memory Organisation like in HPL (LINPACK). Do not pack the A, B, C matrices together but use a
memory organisation like in HPL where the matrices are stored kind of interleaved.

-C (default: disabled)
Call fake LINPACK callback functions. This is used to test the HPL callback implementation.
For internal testing only.

-Ca <int> (default: 0) (HPL-GPU Setting)
Set alternate lookahead threshold. Alternate lookahead mode will be used as soon as matrix_n (the
HPL-GPU value matrix_n becomes smaller than <int>.

-P <int> (default: not used)
LDA=LDB=LDC = val for HPL like memory. Forces the leading dimension of the matrices to a specific value.
If not set the leading dimensions are chosen such that each row starts at a new cache line.

-T (default: disabled)
Allocate Memory using Huge Tables. Turned out not to perform well for some reasons. Better leave it 
deactivated. To activate this feature shared memory segments with huge tables must be provided.

-B (default: disabled) (CAL Runtime only)
Keep DMA Buffers mapped during kernel execution. The Driver Hack is needed for this option. It is
only relevant when using "-o c" which, however, is the default value.

-x <file> (default: not used)
Load Matrix from file.

-- <int> (default: disabled)
Run a torture test with n iterations. The torture test will automatically set "-A -B -p -z -g"
If will use m and n of 86016. If you do not have sufficient memory available you can override
the m and n settings. make sure you specifiy -m and -n after --. Without additional options
a GPU only torture test is started. Using the standard option you can run combined GPU/CPU torture
tests. E.g. a combined torture test with reduced matrix size can be started by:
-- 10 -m 40960 -n 40960 -c -l -se;

-t <int> (default: 0)
Pin GPU thread to core n. The core which is closest to the GPU should be chosen. Mostly 0.
The additional merge threads will use the next possible core then. E.g. running with 1 merge threads
and -1 6 will use cores 6 and 7.

-ts (default: disabled)
Visualize the Thread affinities.

-tr <int> (default: -2)
Pin the GPU device runtime threads to CPU core <int>. Use -2 to use the same core as the CALDGEMM main
thread. This is the default, as this frees the other cores for concurrent DGEMM execution. Set to -1 to
allow the device runtime to utilize all CPU cores (which is the default of the device runtimes).

-K  <int> (default: none)
Pin GPU main thread for DMA handling to core <int>

-Gx <int> (default: not used)
Use CPU cored given for GPU x. The merge threads will use the next cores, ie -G1 12 will do dividebuffer
for GPU 1 on core 12 (if -X) used and mergebuffers on 13 and following. Multiple GPUs assigned to the
same core are automatically grouped correctly. Use multiple times for multiple GPUs, e.g. -G0 0 -G1 12 -G2 12.
In order to use multiple cores for DivideBuffer, you have to enable the multithreaded DivideBuffer
Option (-Z)!

-Ux <int> (default: -1 = auto, see -Gx)
Pin CPU postprocessing threads of GPU x to CPU core <int>, -1 = default mapping
If -Ux and -Gx differ, postprocessing for GPU x happens on core Ux, other tasks happen on core Gx. (See -Gx)

-UAx <int> (default: -1 = no special pinning)
Allocate memory for host buffers for GPU x on CPU die <int>, -1 = default mapping
In grouped DMA mode (see -[) this also defines the cpu core for the host thread responsible for GPU x.

-UBx <int> (default: none)
Set DMA Mapping for GPU x, i.e. the CPU thread responsible for GPU x in parallel DMA mode (See -*) will run
on core UBx.

-V <int> (default: automatic)
Thread save GPU runtime: (0: no, 1: yes, -1: use global lock). This is mostly important for CAL backend.
CAL is not completely thread save, some API functions are not reentrant. -V 0 will use a mutex to protect
this, -V 1 will not use a mutex causing a possible race condition, so it is UNSAFE! For CUDA and OpenCL,
the APIs are thread save, and there is no difference between -V 0 and -V 1.
-V -1 has a different meaning. In that case, CALDGEMM will use a global mutex to protect and serialize all
GPU API calls. This is for debugging in case one suspects the GPU driver to be not reentrant.

-S (deafult: not used)
Set slow CPU option (see below)

-X (default: disabled)
Do not use a round robin scheduler for multi-GPU but split the matrix along the not favored direction and
process each part by a distinct GPU. This saves BBuffers and is usually faster. This is mandatory force
very large matrices.

-Xb <int> (default; 1)
Use a balanced improved scheduler. Only relevant in combinatio with -X. This should always be better.
There are three mode: 0 no balancing, 1 standard balancing, 2 advanced balancing.
1 or 2 should always be better

-E (default: 0)
Define random seed to use for matrix initialization. Use 0 for time.

-O (default: enabled)
Define backend to use. Available options are:
0: CAL
1: OpenCL
2: CUDA
3: fCPU Only

-Oc <int> (default: autodetect) (OpenCL Runtime only)
Set this to 1, to enable the alter GPU_C DMA framework, which keeps the C matrix on the GPU. (See head of
this file!). CAL is forced to -Oc 0, CUDA is forced to -Oc 1, OpenCL can use both and defaults to 0.
It is usually a good idea to enable this with OpenCL, but some drivers do not support it, hence it is
disabled by default.

-Ol <string> (default: none) (OpenCL Runtime only)
Set a 3rd party external library that provides the GPU kernels. If this is not set, reference kernels
(unoptimized) that come with CALDGEMM are used.

-Oe (default: disabled) (OpenCL Runtime only)
Do not allow multiple concurrent OpenCL kernels. Some OpenCL devices are slower when they execute multiple
DGEMM kernels at the same time. This settings uses OpenCL events to enforce serialization of OpenCL
kernels. It does not work well, and should not be used. It is better to enforce serialization on the driver
side, e.g. on AMD cards via GPU_NUM_COMPUTE_RINGS=1 env variable.

-Oq (default: disabled) (OpenCL Runtime only, CUDA support planned)
Use simple GPU Queuing for OpenCL. This comes with less overhead, so it is generally better for the GPU.
But it is incompatible with GPU_C = 0 (-Oc option). If you use -Oc 1, you should also enable this. This
enforces the Improved Scheduler (-X option)

-Op <int> (default: disabled)
CALDGEMM requires certain internal buffers. The number depends on n/h and m/h, i.e. on the matrix size.
As this is not known in advance, these buffers are allocated during runtime. The can be preallocated
during the initialization via -Op option. The maximum number of blocks maximum of(nb = n / h, mb = m/h)
must be pro provided then.

-Oa (default: disabled) (OpenCL Runtime Only, CUDA support planned) (HPL-GPU Setting)
CALDGEMM can run asynchronous side queues on the GPU to offload other tasks concurrent to DGEMM execution
If this is set, DGEMM bench creates an async side queues and uses this queue to test a single-tile dgemm.

-Ox (default: disabled) (OpenCL Runtime Only)
Do not put the CPU in the OpenCL context
Can safe some OpenCL internal buffer sizes. Some OpenCL runtimes fail to allocate the large buffers
required for -Oc 1. You should try whether it works, if yes fine, if not disable it.

-Ot (default: disabled)
Use 3rdPartyTranspose kernel for matrix transposition, which is provided by 3rd party external library
(See -Ol setting)

-F (default: 0)
Define OpenCL Platform ID to use.

-J <int> (default: 0)
Allow small tiles to process the remainder on GPU (0 disable, 1 enable, 2 auto). Auto tries to
find a good tile size automatically, which does not always work. In general, for a system with a fast CPU,
it is best to leave this as 0. That will ensure optimal GPU tile size, the CPU does the rest.
If the GPU is very fast, set this to 1, to ensure that the remainder part processed by the CPU does not
become a bottleneck. In any case, you can try setting 2 when you try to optimize, but the effect is small
and if the prediction fails, it deteriorates performance significantly.

-Q (default: disabled)
Wait for pressing a key before exiting

-! (default: disabled)
Do not use page locked memory

-_ (default: disabled) (OpenCL Runtime and CUDA Runtime only)
Allocate memory using the GPU runtime library (e.g. OpenCL) instead of malloc. This is required for using
GPU_C = 1 (-Oc 1 option) in combination with -o c. In general, it is usually faster with GPU_C = 1 regardless
of whether -o g or -o c is used. Some drivers do not support this properly.

-= <int> (default: 2) (GPU_C = 0 only)
Define number of MergeBuffer threads per GPU.

-% (default: disabled)
Skip CPU Pre- and Postprocessing. Leads to incorrect results.
For internal testing only

-@ <list> (default: disabled)
Comma or Semicolon separated list of CPU cores to exclude. This is usefull if you run something in parallel
to CALDGEMM. Or if you have a bulldozer or HyperThreading GPU and you want to disable all even or all
odd numbered cores. In general, it is a good idea to disable HyperThreading for CALDGEMM.

-. (default: disabled) (CAL Runtime only)
Repin Main Thread During Active Wait for GPU Event. This is a workaround required for the CAL Runtime on
Sandy-Bridge-E CPUs. It costs performance, so only enable when needed.

-~ (default: disabled)
Always repin main thread. This is an alternate workaround for Sandy-Bridge-E CPUs (see -. option)

-, <int> (default: disabled) (CAL Runtime only)
Sleep for n usec during active wait for GPU. This can save some CPU resources at the cost of increased latency.

-: (default: disabled)
Enable NUMA Pinning. This tries to distribute all employed CPU threads evenly among NUMA nodes. Has little
effect, does not always work, but has practically never a negative effect.

-/ <list> (default: disabled)
Comma or Semicolon separated list of GPU devices to use (replaces -y for multiple devices)
Usually, -Y 3 will use GPU devices 0, 1, and 2, while -y 3 will use only GPU device 3.
This gives more fine-grained control on which GPU devices to use. On NUMA systems, it can be beneficial to
interleave devices on different NUMA nodes.

-* <int> (default: 0)
Enable Parallel DMA option if n >= <int>. Set to very large number to enable always.
This mode will use a different CPU core to manage each GPU, thus requiring more CPU resources. The cores are
defined via -UBx option.

-[ <int> (default: 0)
Enable Grouped Parallel DMA option if n < <int>. Requires Parallel DMA option and requires -* > -[ or -* -1.
Set this option to -1 in order to always use Gouped Parallel DMA and never standard Parallel DMA.
This can group the GPUs and use a CPU core for multiple GPUs. Cores defined via -UAx option.
As an example, -* -1 -[ 100000 -UA0 0 -UA1 0 -UA2 10 -UA3 10 -UB0 0 -UB1 1 -UB2 10 -UB3 11, will use 4 threads
(one per gpu) for matrix sizes above 100000 on cores 0, 1, 10, 11 and 2 threads for smaller matrices on cores
0 and 10, with core 0 handling gpus 0 and 1.

-] <int> (default: disabled) (AMD GPUs only, uses ADL library)
Maximum allowed GPU temperature (check applied after one caldgemm iteration, meaningfull in combination with -R)



Other CALDGEMM Options:

The CALDGEMM config allow the SlowCPU option which should be used when the CPU is comparably slow compared
to the GPU. It deactivates 2nd and 3rd phase runs and adjusts the tiling size to minimize the 1st phase
CPU run.

////////////////////////////////////////////////////////////////////////////////////////////////////////////////

Performance Optimization Guide:

To achieve good performance multiple steps should be performed:
0. Update Settings for the GPU used
1. Optimize Kernel Performance.
2. Optimize System Performance of GPU-DGEMM (including DMA-transfer, post-/ preprocessing).
3. Optimize Combined GPU/CPU Performance.
4. Optimize multi-GPU performance.

If you have multiple GPUs better do the following with a single GPU first. Try multiGPU afterwards (step 4).
Add -y 0 to each of the following command lines at the beginning.

In principle, you should try to achieve the following performance:
The kernel performance dictates the final performance. Kernel performance is usually 80%-90% of the
theoretical peak performance of the GPU. The CAL kernel should achieve 574 GFLOPS with 5870 GPU,
623 GFLOPS with 6970 GPU, 805 GFLOPS with 7970 GPU, to give a rough overview.

Goint from single GPU kernel performance to single GPU system performance, you should expect a loss of 1%-3%.
Scaling to multi-GPU should be almost perfect for 2 GPUs (less than 2% loss) and for 4 GPUs you should expect
less than 4% less.

If you then go to HPL, a rough guideline is that HPL should achieve 7%-15% less GFLOPS than DGEMM, while
multi-node HPL will encounter an additional 5%-10% loss.

The following procedure is mostly for CAL. Additional suggestions for OpenCL and CUDA follow later. Still, many
aspects of the CAL guide are also valid for OpenCL / CUDA.

Some general remarks at the beginning:
CALDGEMM by default uses pinned host memory, which cannot be swapped. It might be necessary to set ulimits
accordingly: ulimit -m unlimited; ulimit -l unlimited; ulimit -v unlimited;

Some GPUs throttle themselves during DGEMM execution. For AMD GPUs, you can use the "atitweak" python utility
to modify the GPU poertune feature (atitweak -p) to overcome this. Keep in mind that this might run the GPU our
of spects, so it can damage your hardware if done incorrectly. This is at your own risk. You should at least
monitor temperature constantly if doing so.

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Step 0:
Different GPUs require different settings for optimal performance.

Especially the splitting ratio calculation may not work correctly. Always keep an eye on the GPU time and the
CPU time. If one of them is higher then the other, adjust the -j ratio. This is also relevant for the 5000
series due to different clock speeds.

CALDGEMM comes with Assembler GPU DGEMM kernels for the CAL runtime. Depending on the particular GPU used,
the options in caldgenn_config.h should be adjusted for optimal DGEMM performance.

For the 5xxx series, the following is suggested:
Enable exactly CALDGEMM_TRANSPOSED_B, CALDGEMM_44 as DGEMM kernel settings in caldgemm_config.h
For 5xxx series h can be used almost arbitrarily but is suggested to be at least 1024.
5xxx works well both with -o g and -o c

For the 6xxx series the following configuration is suggested:
Enable CALDGEMM_TRANSPOSED_B, CALDGEMM_44.
It is best to enable the CALDGEMM_44_BT_64 and CALDGEMM_44_BT_64_CONVERT options in caldgemm_config.h.
h = 2304 performs best. 
Use -o c in any case! See that implicit driver sync works (-I), or use DMA fetch queue (-^).

For the 7xxx series, please enable the following settings (default):
CALDGEMM_TRANSPOSED_A, CALDGEMM_44, CALDGEMM_DUAL_ENTRY, CALDGEMM_LATE_EXIT_CONDITION, CALDGEMM_SHIFT_TEXTURE 1
h = 3072 works well.
-o g works usually better than -o c

In general, it is no longer suggested to use CAL. OpenCL and CUDA are the better options.
OpenCL comes only with a reference kernel, it has support to load an optimized kernel from a 3rd party library.
This is the suggested way.
CUDA also comes only with a reference kernel yet, this should be changed to CUBLAS in the future.


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Step 1:
The kernel performance should be good out of the box. Most kernel parameters cannot be changed via
command-line but during compilation in caldgemm_config.h. Usually the parameters are fine as they are.

Run a "./dgemm_bench -v" to check the kernel performance. The kernel will usually write its output to
host memory.

Some systems have a poor DMA. You can try to alter the output to GPU memory and see whether
kernel performance gets better. Run "./dgemm_bench -o g -v" for this. If the second option is better,
always use "-o g". For OpenCL and for 7xxx AMD series and above, -o g is suggested in general.

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Step 2:
Optimize System performance

First check whether DMA is working well. Run "./dgemm_bench -o g -v" and look at the copy speeds from and
to the device. (-o g is required here to measure PCIe speed.)
Anything above 5gb/s should be fine. If the speed is below probably the GPU threads are pinned
to a wrong CPU core on NUMA architectures. You can alter the CPU core with the -t option.
Try "./dgemm_bench -o g -v -t 0", "./dgemm_bench -o g -v -t 1", etc to find the best CPU core.
Using a CPU core other than zero can lead to problems when using GPU/CPU combined DGEMM.

Test you system GPU DGEMM performance. The parameters you definitely want to have are:
-z (multithreading)
-p (memory interleaving)
-A (asynchronous DMA transfer)
Run "./dgemm_bench -z -p -A -m 40960 -n 40960"

This part is only relevant if you found you want to use "-o g" in Step 1:
There is a DMA problem in the AMD driver that can be overcome by a workaround. Usually it is autotedected
whether the workaround can and must be applied. Still, you better recheck by hand.
You can force the workaround using the -I parameter.
Rerun the above test: "./dgemm_bench -z -p -A -m 40960 -n 40960 -o g -I"
If the performance is better you have to check whether the results are correct. The workaround will only
work with some drivers and might produce false results with others. To verify run:
"./dgemm_bench -z -p -A -m 40960 -n 40960 -o g -I -e"

This part is only relevant if you found you want to use "-o c" in Step 1:
Use the AMD driver hack. Apply the hack and then use the "-B" parameter.
Run "./dgemm_bench -z -p -A -B -m 40960 -n 40960". You'll see a warning if the hack was not applied
correctly. Performance is not necessarily better than without "-B" but the CPU load is decreased. You'll
see the difference when using combined CPU/GPU DGEMM.

If you have an Ivy-Bridge system with CAL runtime, add -. option.

On intel systems, you can usually restrict to one output thread with -= 1 option.

If you have much more GPU power then CPU power, -J 1 is suggested, and perhaps disable dynamic CPU/GPU scheduling (no -s).

You can interleave GPUs among numa nodes with -/ setting (see quad-GPU 7xxx series example below).

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Step 3:
Optimize Overall performance.

First check the possible CPU performance: "./dgemm_bench -c -z -p -m 40960 -n 40960".
Then do a combined CPU/GPU run: "./dgemm_bench -c -g -l -s -p -z -A -m 40960 -n 40960".
Use the "-o g", "-I", and "-B" parameters as determined in steps 1 and 2.
The performance should be better than in step 2.

You can alter the CPU/GPU ratio using the "-j" parameter. Try to tune it such that the GPU and CPU DGEMM
times are equal. It is better to set -j rather high, as the dynamic scheduler will compensate this with a
work-stealing algorithm. If you see many 3rd-phase runs in caldgemm output, than "-j" is possibly to big.

If the AMD driver hack is not available, you might get better combined performance by using "-o g"
(foolow the appropriate instructions in step 2 also).

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Step 4:
There is little you can do to optimize multi-GPU performance. You have to determine the CPU core for each GPU
independently. Repeat this part of step2. Use -y 0, -y 1, -y 2 etc to optimize each gpu.
Finally use -G0 ? -G1 ? -G2 ? and insert the optimal cpu core you obtained for each GPU.

Next step is tuning -Ux settings. Try if Parallel DMA mode and grouped DMA mode yield a benefit.

First try to run without CPU. From now on omit the "-y 0". The performance should scale almost linearly with
multi-GPU.

You can try the -X and -Z options. They usually increase performance for 3 GPUs or more. You might also
want to increase w. w = 1536 or w = 2048 can achieve good performance. For larger w a smaller h is suggested.
Try h = 3072 for instance.

If you have good multi-GPU performance try to use the CPU as well. You might need to change the -j value.
Best, start with -j 1 to do almost all work of the CPU. Then decrease j step by step until you see optimal
performance. (-j 0.97 ... -j 0.94 ... -j 0.91).

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Guidelines for OpenCLL / CUDA:
The CUDA part is not fully implemented yet. This guide is written as if it was fully integrated, feel free to
implement the missing features for CUDA yourself :|.

The most important thing for OpenCL is the 3rd party library for the DGEMM kernel. CALDGEMM itself comes only
with an unoptimized reference implementation. There is a sample 3rd party library with a template that shows
how such a library has to work. In caldgemm_config.h there are also some options to tweak the integrated OpenCL
kernels's performance. Important aspects here are ENABLE_TILED_KERNEL and DISABLE_SIMPLE_BUFFERS, but
performance will anyway be much less than with proper 3r party kernel.

In general, you should try to use OpenCL with GPU_C = 1. It is almost always better. Only in the case of a com-
paratively fast CPU (like 2 * 12 core CPU + slow GPU like 5870), the GPU_C = 0 option is possibly faster.
In general, GPU_C = 0 works better with CAL, which is usually around 5% faster than OpenCL. So if you want to
test is, CAL is probably the way to go (although no longer supported for newer GPUs).

OpenCL with GPU_C = 0 setting has almost identical behavior as CAL, so please follow the above guide.
The following refers to OpenCL with GPU_C = 1 and to CUDA.

OpenCL with GPU_C = 1 will transfers tile of the C matrix completely to the GPU using strided submatrix transfers.
There are no intermediate host buffers.

Therefore, there are no pre-/postprocessing threads on the CPU.

Due to the nature of GPU_C = 1,  the GPU pinnin has practically no influence (except perhaps of
device API internal buffers, which can be pinned to the either or the other GPU.) Hence, it makes sense to set
the -UAx settings as described above for the -Gx settings, as it comes at zero cost. But it is not really necessary.
It works well without.

In general, you will want to use device-runtime-allocated memory. It is usually mich faster than plain malloc.
The -o c setting enforces device-runtime-allocated memory for OpenCL in any case.
For this you need the -_ option. Be aware that some OpenCL drivers have problems allocated the large buffers required.
If this leads to memory allocation problems, you should first try to fix this driver issue before you start to
disable device-runtime-allocated memory.

The baseline for OpenCL will thus be something like

./dgemm_bench -O 1 -Oc 1 -o g -_ -Ol my_opencl_3rd_party_lib.so -w 1920 -h 3072 -UAx... -A -c -z -X -p -m ... -n ...

The most relevant optimization settings are:
-Oq (enable simple queuing, almost always faster)
-J 1 (enable small tiles)
-bb ? (choose correct number of bbuffers)
-Op ? (chose correct preallocation setting)
-Ox (exclude CPU from context)
-Ot (improved transposition kernel)
-Xb 1/2 (improved scheduler balancing)

Of course you do not need -X -Xb if you use only a single GPU

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Examples:

Measure kernel and PCIe performance:
./dgemm_bench -o g -v

Run GPU-only DGEMM
./dgemm_bench -z -p -A -B -m 40960 -n 40960
./dgemm_bench -z -p -A -o g -I -m 40960 -n 40960

Run CPU/GPU DGEMM
./dgemm_bench -c -g -z -s -l -p -A -B -y -1 -j -1 -m 40960 -n 40960
./dgemm_bench -c -g -z -s -l -p -A -o g -I -y -1 -j -1 -m 40960 -n 40960

Run Multi-GPU/CPU DGEMM
./dgemm_bench -c -g -z -s -l -p -A -B -m 89088 -n 89088 -X -Z -w 1536 -h 3072 -G0 0 -G1 0 -G2 12 -j 0.91

Example of quad-GPU without CPU 6xxx series (2 * 12 core AMD Magny-Cours system, GPUs 0,1, connected to numa node 0 (cores 0-11))
./dgemm_bench -g -A -Z -X -p -w 2048 -h 2304 -o g -I 1 -4 123000 -G0 0 -G1 12 -G2 1 -G3 13 -U0 2 -U1 14 -U2 4 -U3 16 -UA0 0 -UA1 12 -UA2 0 -UA3 12 -K 0 -z -= 2 -/ 0,2,1,3 -J 1

Example of quad-GPU+CPU 7xxx series DGEMM (2 * 8 core numa Ivy-Bridge system, GPUs 0,1 connected to numa node 0 (cores 0-7))
./dgemm_bench -g -A -Z -X -p -w 1920 -h 3072 -o g -I 1 -4 123000 -G0 0 -G1 8 -G2 0 -G3 8 -U0 1 -U1 9 -U2 2 -U3 10 -UA0 0 -UA1 8 -UA2 0 -UA3 8 -K 0 -z -= 1 -. -/ 0,2,1,3 -c -j 0.955 -J 1

Example as above, with Parallel DMA mode
./dgemm_bench -g -A -Z -X -p -w 1920 -h 3072 -o g -I 1 -4 123000 -G0 0 -G1 8 -G2 0 -G3 8 -U0 1 -U1 9 -U2 2 -U3 10 -UA0 0 -UA1 8 -UA2 0 -UA3 8 -UB1 8 -UB2 4 -UB3 12 -K 0 -z -= 1 -. -/ 0,2,1,3 -* 1000000 -c -j 0.955 -J 1

Example as above, with grouped DMA mode
./dgemm_bench -g -A -Z -X -p -w 1920 -h 3072 -o g -I 1 -4 123000 -G0 0 -G1 8 -G2 0 -G3 8 -U0 1 -U1 9 -U2 2 -U3 10 -UA0 0 -UA1 8 -UA2 0 -UA3 8 -UB1 8 -UB2 4 -UB3 12 -K 0 -z -= 1 -. -/ 0,2,1,3 -* 1000000 -[ 1000000 -c -j 0.955 -J 1

GPU only torture test for device 0
./dgemm_bench -y 0 -- 100

GPU/CPU torture test
./dgemm_bench -- 100 -c -g -s -z -p

Single GPU with OpenCL
./dgemm_bench -O 1 -w 1920 -h 2976 -_ -Ol amddgemm_hawai.so -A -z -p -g -o g -6 20

Single GPU with OpenCL and advanced options
./dgemm_bench -O 1 -w 1920 -h 2976 -_ -Ol amddgemm_hawai.so -A -z -p -g -J 1 -: -o g -6 20 -Oq -bb 15 -Op 20 -Ox -Ot

Multi-GPU with OpenCL (2 * 10 core numa system)
./dgemm_bench -O 1 -w 1920 -h 2976 -_ -Ol amddgemm_hawai.so -A -z -p -X -Xb 2 -g -J 1 -: -UA0 0 -UA1 10 -UA2 0 -UA3 10 -K 0 -/ 0,2,1,3 -o g -6 58 -Oq -bb 15 -Op 60 -Ox -Ot -Y 4 

Multi-GPU + CPU with OpenCL
./dgemm_bench -O 1 -w 1920 -h 2976 -_ -Ol amddgemm_hawai.so -A -z -p -X -Xb 2 -g -J 1 -: -UA0 0 -UA1 10 -UA2 0 -UA3 10 -K 0 -/ 0,2,1,3 -o g -6 58 -Oq -bb 15 -Op 60 -Ox -Ot -Y 4 -c -j 0.972

Since the full list of parameters can be a bit overwhelming, it follows a list of common parameters required for good performance:

General Parameters: -? -e -o -h -w -l -m -n -v -R -y -Y -bb -d -z -c -g -f -j -p -1 -2 -4 -6 -K -X -Xb -O -J -: -/ -@ -]
Parameters for CAL / GPU_C = 0: -I -^ -Z -s -M -N -rr -B -Gx -Ux -UAx -UBx -. -= -* -[
Parameters for OpenCL / CUDA / GPU_C = 1: -tr -UAx -Oc -Oq -Ol -Op -Ox -Ot -F -_
Parameters for HPL: -Ca -Oa

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