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211f05fb09
openmpi/ompi/mca/coll/ml/coll_ml.h
Nathan Hjelm 5f1254d710 Update code base to use the new opal_free_list_t 
Use of the old ompi_free_list_t and ompi_free_list_item_t is
deprecated. These classes will be removed in a future commit.

This commit updates the entire code base to use opal_free_list_t and
opal_free_list_item_t.

Notes:

OMPI_FREE_LIST_*_MT -> opal_free_list_* (uses opal_using_threads ())

Signed-off-by: Nathan Hjelm <hjelmn@lanl.gov>
2015-02-24 10:05:45 -07:00

1023 строки
36 KiB
C
Исходник Ответственный История

/* -*- Mode: C; c-basic-offset:4 ; indent-tabs-mode:nil -*- */
/*
* Copyright (c) 2009-2012 Oak Ridge National Laboratory. All rights reserved.
* Copyright (c) 2009-2012 Mellanox Technologies. All rights reserved.
* Copyright (c) 2013-2015 Los Alamos National Security, LLC. All rights
* reserved.
* Copyright (c) 2014 Research Organization for Information Science
* and Technology (RIST). All rights reserved.
* $COPYRIGHT$
*
* Additional copyrights may follow
*
* $HEADER$
*/
/** @file */
#ifndef MCA_COLL_ML_ML_H
#define MCA_COLL_ML_ML_H
#include "ompi_config.h"
#include "opal/mca/mca.h"
#include "opal/mca/base/base.h"
#include "opal/datatype/opal_convertor.h"
#include "opal/threads/mutex.h"
#include "ompi/mca/coll/coll.h"
#include "ompi/request/request.h"
#include "ompi/mca/bcol/bcol.h"
#include "ompi/mca/sbgp/sbgp.h"
#include "ompi/op/op.h"
#include "opal/class/opal_free_list.h"
#include "coll_ml_lmngr.h"
#include "coll_ml_functions.h"
#include "coll_ml_colls.h"
#include "coll_ml_allocation.h"
#include "coll_ml_config.h"
BEGIN_C_DECLS
/* macros for return status */
enum {
ML_OMPI_COMPLETE = 1,
ML_OMPI_INCOMPLETE
};
enum {
ML_SMALL_MSG,
ML_LARGE_MSG,
ML_NUM_MSG
};
/* ML collectives IDs */
enum {
/* blocking functions */
ML_ALLGATHER,
ML_ALLGATHERV,
ML_ALLREDUCE,
ML_ALLTOALL,
ML_ALLTOALLV,
ML_ALLTOALLW,
ML_BARRIER,
ML_BCAST,
ML_EXSCAN,
ML_GATHER,
ML_GATHERV,
ML_REDUCE,
ML_REDUCE_SCATTER,
ML_SCAN,
ML_SCATTER,
ML_SCATTERV,
ML_FANIN,
ML_FANOUT,
/* nonblocking functions */
ML_IALLGATHER,
ML_IALLGATHERV,
ML_IALLREDUCE,
ML_IALLTOALL,
ML_IALLTOALLV,
ML_IALLTOALLW,
ML_IBARRIER,
ML_IBCAST,
ML_IEXSCAN,
ML_IGATHER,
ML_IGATHERV,
ML_IREDUCE,
ML_IREDUCE_SCATTER,
ML_ISCAN,
ML_ISCATTER,
ML_ISCATTERV,
ML_IFANIN,
ML_IFANOUT,
ML_NUM_OF_FUNCTIONS
};
/* ML broadcast algorithms */
enum {
COLL_ML_STATIC_BCAST,
COLL_ML_SEQ_BCAST,
COLL_ML_UNKNOWN_BCAST,
};
struct mca_bcol_base_module_t;
/* collective function arguments - gives
* one function signature for calling all collective setup
* routines, with the initial call to a collective function having
* the context to access the right parts of the data structure.
* this information is used by each of the setup functions to
* setup the correct information for each of the functions in the
* hierarchy that will be called. */
/* RLG NOTE: Need to figure out what arguments to store here,
* and which ones directly in the message descriptor
*/
struct mpi_coll_fn_params_t {
union {
struct {
ompi_communicator_t *comm;
int n_fanin_steps;
int n_fanout_steps;
int n_recursive_doubling_steps;
} ibarrier_recursive_doubling;
struct {
int root;
ompi_communicator_t *comm;
struct ompi_datatype_t *datatype;
} ibcast;
} coll_fn;
};
typedef struct mpi_coll_fn_params_t mpi_coll_fn_params_t;
/* algorithm parmeters needed for the setup function */
struct mpi_coll_algorithm_params_t {
union {
struct {
int n_fanin_steps;
int n_fanout_steps;
int n_recursive_doubling_steps;
} ibarrier_recursive_doubling;
struct {
int place_holder;
} ibcast;
} coll_fn;
};
typedef struct mpi_coll_algorithm_params_t mpi_coll_algorithm_params_t;
/* setup function - used to setup each segment (or fragment)
* to be processed
*/
struct mca_coll_ml_module_t;
struct mca_coll_ml_topology_t;
typedef int (*coll_fragment_comm_setup_fn)(struct mca_coll_ml_module_t *ml_module,
mpi_coll_fn_params_t *fn_params, mpi_coll_algorithm_params_t *algorithm_params);
/* full collective description */
struct coll_ml_collective_description_t {
/* number of temp buffers */
int n_buffers;
/* description size */
int n_functions;
/* collective setup function - called for every non-blocking
* function, and for each fragment of such a message
*/
coll_fragment_comm_setup_fn *coll_fn_setup_fn;
/* algorithm parameters */
mpi_coll_algorithm_params_t alg_params;
/* list of functions */
mca_bcol_base_function_t *functions;
/* function names - for debugging */
char **function_names;
/* Signalling collective completion */
bool completion_flag;
};
typedef struct coll_ml_collective_description_t coll_ml_collective_description_t;
/* Utility data structure */
struct rank_properties_t {
int rank;
int leaf;
int num_of_ranks_represented;
}; typedef struct rank_properties_t rank_properties_t;
/* data structure for holding node information for the nodes of the
* hierarchical communications tree.
*/
struct sub_group_params_t {
/* rank of root in the communicator */
int root_rank_in_comm;
/* index in subgroup */
int root_index;
/* number of ranks in subgroup */
int n_ranks;
/* index of the first element in the subgroup. The
* assumption is that
* ranks for all subgroups are stored in a single
* linear array
*/
int index_of_first_element;
/*
* level in the hierarchy - subgroups at the same
* level don't overlap. May not be the same as the
* sbgp level.
*/
int level_in_hierarchy;
/*
* Information on the ranks in the subgroup. This includes
* the rank, and wether or not the rank is a source/sink of
* of data in this subgroup, or just a "pass through".
*/
rank_properties_t *rank_data;
/* level one index - for example,
for( i = 0; i < level_one_index; i++) will loop
through all level one subgroups, this is significant
since level one is a disjoint partitioning of all ranks
i.e. all ranks appear once and only once at level one
*/
int level_one_index;
};
typedef struct sub_group_params_t sub_group_params_t;
/* function to setup information on the order of a given bcol within
* a specific ML-level algorithm.
*/
int mca_coll_ml_setup_scratch_vals(mca_coll_ml_compound_functions_t *func_list,
int *scratch_indx, int *scratch_num, int n_hiers);
/* driver for setting up collective communication description */
int ml_coll_schedule_setup(struct mca_coll_ml_module_t *ml_module);
int ml_coll_up_and_down_hier_setup(
struct mca_coll_ml_module_t *ml_module,
struct mca_coll_ml_topology_t *topo_info,
int up_function_idx,
int top_function_idx,
int down_function_idx,
int collective);
int ml_coll_barrier_constant_group_data_setup(
struct mca_coll_ml_topology_t *topo_info,
mca_coll_ml_collective_operation_description_t *schedule);
/* Barrier */
int ml_coll_hier_barrier_setup(struct mca_coll_ml_module_t *ml_module);
/* allreduce */
int ml_coll_hier_allreduce_setup(struct mca_coll_ml_module_t *ml_module);
int ml_coll_hier_allreduce_setup_new(struct mca_coll_ml_module_t *ml_module);
void ml_coll_hier_allreduce_cleanup_new(struct mca_coll_ml_module_t *ml_module);
/* alltoall */
int ml_coll_hier_alltoall_setup(struct mca_coll_ml_module_t *ml_module);
int ml_coll_hier_alltoall_setup_new(struct mca_coll_ml_module_t *ml_module);
/* allgather */
int ml_coll_hier_allgather_setup(struct mca_coll_ml_module_t *ml_module);
void ml_coll_hier_allgather_cleanup(struct mca_coll_ml_module_t *ml_module);
/* gather */
int ml_coll_hier_gather_setup(struct mca_coll_ml_module_t *ml_module);
/* broadcast */
int ml_coll_hier_bcast_setup(struct mca_coll_ml_module_t *ml_module);
void ml_coll_hier_bcast_cleanup(struct mca_coll_ml_module_t *ml_module);
/* reduce */
int ml_coll_hier_reduce_setup(struct mca_coll_ml_module_t *ml_module);
void ml_coll_hier_reduce_cleanup(struct mca_coll_ml_module_t *ml_module);
/* reduce */
int ml_coll_hier_scatter_setup(struct mca_coll_ml_module_t *ml_module);
/* alltoall */
int mca_coll_ml_alltoall(void *sbuf, int scount,
struct ompi_datatype_t *sdtype,
void* rbuf, int rcount,
struct ompi_datatype_t *rdtype,
struct ompi_communicator_t *comm,
mca_coll_base_module_t *module);
int mca_coll_ml_alltoall_nb(void *sbuf, int scount,
struct ompi_datatype_t *sdtype,
void* rbuf, int rcount,
struct ompi_datatype_t *rdtype,
struct ompi_communicator_t *comm,
ompi_request_t **req,
mca_coll_base_module_t *module);
/* allgather */
int mca_coll_ml_allgather(void *sbuf, int scount,
struct ompi_datatype_t *sdtype,
void* rbuf, int rcount,
struct ompi_datatype_t *rdtype,
struct ompi_communicator_t *comm,
mca_coll_base_module_t *module);
/* non-blocking allgather */
int mca_coll_ml_allgather_nb(void *sbuf, int scount,
struct ompi_datatype_t *sdtype,
void* rbuf, int rcount,
struct ompi_datatype_t *rdtype,
struct ompi_communicator_t *comm,
ompi_request_t **req,
mca_coll_base_module_t *module);
/* gather */
int mca_coll_ml_gather(void *sbuf, int scount,
struct ompi_datatype_t *sdtype,
void* rbuf, int rcount,
struct ompi_datatype_t *rdtype,
int root,
struct ompi_communicator_t *comm,
mca_coll_base_module_t *module);
/* nonblocking Barrier */
int ml_coll_hier_nonblocking_barrier_setup(struct mca_coll_ml_module_t *ml_module, struct mca_coll_ml_topology_t *topo_info);
/* Memory syncronization collective setup */
int ml_coll_memsync_setup(struct mca_coll_ml_module_t *ml_module);
/* Fragment descriptor */
struct mca_coll_ml_descriptor_t;
struct mca_coll_ml_fragment_t {
opal_list_item_t super;
struct mca_coll_ml_descriptor_t *full_msg_descriptor;
int offset; /*offset for progress pointer*/
int length; /*fragment length I assume*/
opal_convertor_t convertor; /*convertor for copy/pack data*/
/* current function index */
int current_fn_index;
/* array of function arguments */
struct bcol_function_args_t *fn_args;
};
typedef struct mca_coll_ml_fragment_t mca_coll_ml_fragment_t;
OMPI_DECLSPEC OBJ_CLASS_DECLARATION(mca_coll_ml_fragment_t);
#define MCA_COLL_ML_NO_BUFFER -1
#define MCA_COLL_IBOFFLOAD_SET_ML_BUFFER_INFO(coll_op, index, desc) \
do { \
(coll_op)->variable_fn_params.buffer_index = index; \
(coll_op)->fragment_data.buffer_desc = desc; \
/* pasha - why we duplicate it ? */ \
(coll_op)->variable_fn_params.src_desc = desc; \
(coll_op)->variable_fn_params.hier_factor = 1; \
(coll_op)->variable_fn_params.need_dt_support = false; \
} while (0)
/*Full message descriptor*/
struct mca_coll_ml_descriptor_t {
ompi_request_t super; /*base request*/
struct ompi_datatype_t *datatype; /*ompi datatype*/
size_t count; /*count of user datatype elements*/
uint32_t sequence_num; /*sequence number for collective operation*/
size_t frags_limit; /*upper limit on # of fragments*/
size_t frags_start; /*number of fragments started*/
/*number of fragments completed*/
size_t frags_complete;
/* number of fragments needed to process this message */
size_t n_fragments;
volatile bool free_resource; /*signals release resource*/
/*pointer to reduction operation, e.g. MPI_MIN - need to handle
* user defined functions also */
/* ompi_predefined_op_t *operation; */
/*pointer to a communication schedule, data struct undefined*/
struct coll_ml_collective_description_t *local_comm_description;
/* fragment descriptor - we always have a fragment descriptor
* if we get a full message descriptor. Optimization for
* small messages */
mca_coll_ml_fragment_t fragment;
/* The ML memory buffer index that should consist the send and
recv information
if the index is -1, it means no buffer was allocated */
uint64_t buffer_index;
};
typedef struct mca_coll_ml_descriptor_t mca_coll_ml_descriptor_t;
OMPI_DECLSPEC OBJ_CLASS_DECLARATION(mca_coll_ml_descriptor_t);
/* sbgp and bcol module pairs */
struct hierarchy_pairs {
mca_sbgp_base_module_t *subgroup_module;
struct mca_bcol_base_module_t **bcol_modules;
int num_bcol_modules;
int bcol_index;
mca_bcol_base_component_t *bcol_component;
};
typedef struct hierarchy_pairs hierarchy_pairs;
/* list of ranks in each group */
struct ml_level_t {
int n_modules;
hierarchy_pairs *modules;
};
typedef struct ml_level_t ml_level_t;
enum {
COLL_ML_HR_FULL, /* Full hierarchy topology, all bcols and sbgps attends in discovery */
COLL_ML_HR_ALLREDUCE,
COLL_ML_HR_NBS, /* All hierarchy except base socket */
COLL_ML_HR_SINGLE_PTP, /* Single flat ptp hierarchy */
COLL_ML_HR_SINGLE_IBOFFLOAD, /* Single flat iboffload hierarchy */
COLL_ML_TOPO_MAX
};
/* Topology-hierarchy discovery function */
struct mca_coll_ml_module_t; /* forward declaration for the function */
typedef int (* mca_coll_topo_discovery_fn_t)
(struct mca_coll_ml_module_t *ml_module, int n_hierarchies);
typedef enum {
COLL_ML_TOPO_DISABLED = 0,
COLL_ML_TOPO_ENABLED = 1
} topo_status_t;
/**
* Structure to hold the sm coll component. First it holds the
* base coll component, and then holds a bunch of
* sm-coll-component-specific stuff (e.g., current MCA param
* values).
*/
struct mca_coll_ml_component_t {
/** Base coll component */
mca_coll_base_component_2_0_0_t super;
/** MCA parameter: Priority of this component */
int ml_priority;
/** MCA parameter: subgrouping components to use */
char *subgroups_string;
/** MCA parameter: basic collectives components to use */
char *bcols_string;
/** verbosity level */
int verbose;
/** max of communicators available to run ML */
unsigned int max_comm;
/** min size of comm to be available to run ML */
int min_comm_size;
/* base sequence number to use - the expectation is that
* this will be used as a basis for generating IDs for
* specific collective operations
*/
int64_t base_sequence_number;
/** memory pool */
mca_coll_ml_lmngr_t memory_manager;
/* We need it because some bcols cannot
support all possible allreduce data types */
bool need_allreduce_support;
int use_knomial_allreduce;
/* use hdl_framework */
bool use_hdl_bcast;
/* Enable / Disable fragmentation (0 - off, 1 - on, 2 - auto) */
int enable_fragmentation;
/* Broadcast algorithm */
int bcast_algorithm;
/* frag size that is used by list memory_manager */
size_t lmngr_block_size;
/* alignment that is used by list memory_manager */
size_t lmngr_alignment;
/* list size for memory_manager */
size_t lmngr_size;
/* number of payload memory banks */
int n_payload_mem_banks;
/* number of payload buffers per bank */
int n_payload_buffs_per_bank;
/* size of payload buffer */
unsigned long long payload_buffer_size;
/* pipeline depth for msg fragmentation */
int pipeline_depth;
/* Free list tunings */
int free_list_init_size;
int free_list_grow_size;
int free_list_max_size;
/*
* queues for asynchronous collective progress
*/
/* tasks that have not started, either because dependencies are not
* statisfied, or resources are lacking
*/
opal_list_t pending_tasks;
opal_mutex_t pending_tasks_mutex;
/* active incomplete tasks */
opal_list_t active_tasks;
opal_mutex_t active_tasks_mutex;
/* sequential collectives to progress */
opal_list_t sequential_collectives;
opal_mutex_t sequential_collectives_mutex;
bool progress_is_busy;
/* Temporary hack for IMB test - not all bcols have allgather */
bool disable_allgather;
/* Temporary hack for IMB test - not all bcols have alltoall */
bool disable_alltoall;
/* Disable Reduce */
bool disable_reduce;
/* Brucks alltoall mca and other params */
int use_brucks_smsg_alltoall;
mca_coll_topo_discovery_fn_t topo_discovery_fn[COLL_ML_TOPO_MAX];
/* Configure file for collectives */
char *config_file_name;
per_collective_configuration_t coll_config[ML_NUM_OF_FUNCTIONS][ML_NUM_MSG];
};
/**
* Convenience typedef
*/
typedef struct mca_coll_ml_component_t mca_coll_ml_component_t;
/**
* Global component instance
*/
OMPI_MODULE_DECLSPEC extern mca_coll_ml_component_t mca_coll_ml_component;
struct mca_coll_ml_leader_offset_info_t {
size_t offset;
int level_one_index;
bool leader;
};
typedef struct mca_coll_ml_leader_offset_info_t mca_coll_ml_leader_offset_info_t;
/* Topolody data structure */
struct mca_coll_ml_topology_t {
topo_status_t status; /* 0 - enabled , 1 - disabled */
/* information on the selected groups - needed for collective
** algorithms */
int32_t global_lowest_hier_group_index;
int32_t global_highest_hier_group_index;
int number_of_all_subgroups;
int n_levels;
/* bcols bits that describe supported features/modes */
uint64_t all_bcols_mode;
mca_bcol_base_route_info_t *route_vector;
coll_ml_collective_description_t *hierarchical_algorithms[BCOL_NUM_OF_FUNCTIONS];
sub_group_params_t *array_of_all_subgroups;
/* (sbgp, bcol) pairs */
hierarchy_pairs *component_pairs;
/* ordering of ranks when I am the root of the operation.
* This ordering guarantees that data need to be re-ordered
* only at the first or last step in rooted operations,
* depending on whether the opearation is a scatter or
* gather operation.
*/
int *sort_list;
mca_coll_ml_leader_offset_info_t *hier_layout_info;
/* are ranks laid out contiguously */
bool ranks_contiguous;
struct ordering_info_t {
int next_inorder;
int next_order_num;
int num_bcols_need_ordering;
} topo_ordering_info;
};
typedef struct mca_coll_ml_topology_t mca_coll_ml_topology_t;
struct mca_coll_ml_bcol_list_item_t {
opal_list_item_t super;
mca_bcol_base_module_t *bcol_module;
};
typedef struct mca_coll_ml_bcol_list_item_t mca_coll_ml_bcol_list_item_t;
OPAL_DECLSPEC OBJ_CLASS_DECLARATION(mca_coll_ml_bcol_list_item_t);
#define MCA_COLL_MAX_NUM_COLLECTIVES 40 /* ... I do not remember how much exactly collectives do we have */
#define MCA_COLL_MAX_NUM_SUBTYPES 15 /* Maximum number of algorithms per collective */
struct mca_coll_ml_module_t {
/* base structure */
mca_coll_base_module_t super;
/* ML module status - 0 was not initialized, 1 - was initialized */
bool initialized;
/* communicator */
struct ompi_communicator_t *comm;
/* reference convertor */
opal_convertor_t *reference_convertor;
mca_coll_ml_topology_t topo_list[COLL_ML_TOPO_MAX];
/* Collectives - Topology map */
int collectives_topology_map
[MCA_COLL_MAX_NUM_COLLECTIVES][MCA_COLL_MAX_NUM_SUBTYPES];
/* largest number of function calls for the collective routines.
* This is used to allocate resources */
int max_fn_calls;
/* collective sequence number - unique id for barrier type operations */
int32_t no_data_collective_sequence_num;
/* collective sequence number - unique id for each collective */
int32_t collective_sequence_num;
/** ompi free list of full message descriptors **/
opal_free_list_t message_descriptors;
/** ompi free list of message fragment descriptors **/
opal_free_list_t fragment_descriptors;
/** pointer to the payload memory block **/
struct mca_bcol_base_memory_block_desc_t *payload_block;
/** the maximum size of collective function description */
int max_dag_size;
/** data used to initialize coll_ml_collective_descriptors */
struct coll_desc_init {
int max_dag_size;
size_t max_n_bytes_per_proc_total;
mca_coll_base_module_t *bcol_base_module;
} coll_desc_init_data;
/** collective operation descriptor free list - used to manage a single
* collective operation. */
opal_free_list_t coll_ml_collective_descriptors;
/** multiple function collective operation support */
/** broadcast */
mca_coll_ml_collective_operation_description_t *
coll_ml_bcast_functions[ML_NUM_BCAST_FUNCTIONS];
/* bcast size selection criteria - cutoff for the largest size of
* data for which to apply the specified collective operation.
* This gives us the ability to choose algorithm based on size */
size_t bcast_cutoff_size[ML_N_DATASIZE_BINS];
/** Allreduce functions */
mca_coll_ml_collective_operation_description_t *
coll_ml_allreduce_functions[ML_NUM_ALLREDUCE_FUNCTIONS];
/** Reduce functions */
mca_coll_ml_collective_operation_description_t *
coll_ml_reduce_functions[ML_NUM_REDUCE_FUNCTIONS];
/** scatter */
mca_coll_ml_collective_operation_description_t *
coll_ml_scatter_functions[ML_NUM_SCATTER_FUNCTIONS];
/** alltoall */
mca_coll_ml_collective_operation_description_t *
coll_ml_alltoall_functions[ML_NUM_ALLTOALL_FUNCTIONS];
/** allgather */
mca_coll_ml_collective_operation_description_t *
coll_ml_allgather_functions[ML_NUM_ALLGATHER_FUNCTIONS];
/** gather */
mca_coll_ml_collective_operation_description_t *
coll_ml_gather_functions[ML_NUM_GATHER_FUNCTIONS];
/** Barrier */
mca_coll_ml_collective_operation_description_t *
coll_ml_barrier_function;
/** ML Memory Syncronization collective operation */
mca_coll_ml_collective_operation_description_t *
coll_ml_memsync_function;
/** The table of allreduce functions for specific type and op **/
bool allreduce_matrix[OMPI_OP_NUM_OF_TYPES][OMPI_DATATYPE_MAX_PREDEFINED][BCOL_NUM_OF_ELEM_TYPES];
/* data offset from ML */
int32_t data_offset;
int small_message_thresholds[BCOL_NUM_OF_FUNCTIONS];
/* fragmenation parameters */
int use_user_buffers;
uint64_t fragment_size;
uint32_t ml_fragment_size;
/* Bcast index table. Pasha: Do we need to define something more generic ?
the table x 2 (large/small)*/
int bcast_fn_index_table[2];
/* List of pointer to bcols that have been initilized and used.
* So far we use it only for ML memory management */
opal_list_t active_bcols_list;
/* Buffer size required for Bruck's algorithm */
int brucks_buffer_threshold_const;
/* log comm size */
/* We require this for alltoall algorithm */
int log_comm_size;
/* On this list we keep coll_op descriptors that were not
* be able to start, since no ml buffers were available */
opal_list_t waiting_for_memory_list;
/* fallback collectives */
mca_coll_base_comm_coll_t fallback;
};
typedef struct mca_coll_ml_module_t mca_coll_ml_module_t;
OBJ_CLASS_DECLARATION(mca_coll_ml_module_t);
/* query to see if the component is available for use, and can
* satisfy the thread and progress requirements
*/
int mca_coll_ml_init_query(bool enable_progress_threads,
bool enable_mpi_threads);
/* query to see if the module is available for use on the given
* communicator, and if so, what it's priority is. This is where
* the backing shared-memory file is created.
*/
mca_coll_base_module_t *
mca_coll_ml_comm_query(struct ompi_communicator_t *comm, int *priority);
/* Barrier - blocking */
int mca_coll_ml_barrier_intra(struct ompi_communicator_t *comm,
mca_coll_base_module_t *module);
/* Barrier - non-blocking */
int mca_coll_ml_ibarrier_intra(struct ompi_communicator_t *comm,
ompi_request_t **req,
mca_coll_base_module_t *module);
/* Allreduce with EXTRA TOPO using - blocking */
int mca_coll_ml_allreduce_dispatch(void *sbuf, void *rbuf, int count,
struct ompi_datatype_t *dtype, struct ompi_op_t *op,
struct ompi_communicator_t *comm, mca_coll_base_module_t *module);
/* Allreduce with EXTRA TOPO using - Non-blocking */
int mca_coll_ml_allreduce_dispatch_nb(void *sbuf, void *rbuf, int count,
ompi_datatype_t *dtype, ompi_op_t *op,
ompi_communicator_t *comm,
ompi_request_t **req,
mca_coll_base_module_t *module);
/* Allreduce - blocking */
int mca_coll_ml_allreduce(void *sbuf, void *rbuf, int count,
struct ompi_datatype_t *dtype, struct ompi_op_t *op,
struct ompi_communicator_t *comm,
mca_coll_base_module_t *module);
/* Allreduce - Non-blocking */
int mca_coll_ml_allreduce_nb(void *sbuf, void *rbuf, int count,
struct ompi_datatype_t *dtype, struct ompi_op_t *op,
struct ompi_communicator_t *comm,
ompi_request_t **req,
mca_coll_base_module_t *module);
/* Reduce - Blocking */
int mca_coll_ml_reduce(void *sbuf, void *rbuf, int count,
struct ompi_datatype_t *dtype, struct ompi_op_t *op,
int root, struct ompi_communicator_t *comm,
mca_coll_base_module_t *module);
int mca_coll_ml_reduce_nb(void *sbuf, void *rbuf, int count,
struct ompi_datatype_t *dtype, struct ompi_op_t *op,
int root, struct ompi_communicator_t *comm,
ompi_request_t **req,
mca_coll_base_module_t *module);
int mca_coll_ml_memsync_intra(mca_coll_ml_module_t *module, int bank_index);
int coll_ml_progress_individual_message(mca_coll_ml_fragment_t *frag_descriptor);
/*
* the ml entry point for the broadcast function
*/
int mca_coll_ml_parallel_bcast(void *buf, int count, struct ompi_datatype_t *dtype,
int root, struct ompi_communicator_t *comm,
mca_coll_base_module_t *module);
int mca_coll_ml_parallel_bcast_nb(void *buf, int count, struct ompi_datatype_t *dtype,
int root, struct ompi_communicator_t *comm,
ompi_request_t **req,
mca_coll_base_module_t *module);
int mca_coll_ml_bcast_sequential_root(void *buf, int count, struct ompi_datatype_t *dtype,
int root, struct ompi_communicator_t *comm,
mca_coll_base_module_t *module);
/*
* The ml function interface for non-blocking routines
*/
int mca_coll_ml_bcast_unknown_root_nb(void *buf, int count, struct ompi_datatype_t *dtype,
int root, struct ompi_communicator_t *comm,
ompi_request_t **req,
mca_coll_base_module_t *module);
int mca_coll_ml_bcast_known_root_nb(void *buf, int count, struct ompi_datatype_t *dtype,
int root, struct ompi_communicator_t *comm,
ompi_request_t **req,
mca_coll_base_module_t *module);
OMPI_DECLSPEC int mca_coll_ml_bcast_unknown_root_with_frags_nb(void *buf, int count,
struct ompi_datatype_t *dtype,
int root, struct ompi_communicator_t *comm,
ompi_request_t **req, mca_coll_base_module_t *module);
/* This routine sets up a sequential hierarchical scatter algorithm. The
* assumptions are that each rank knows in which sub-group that data will show
* up first, and that the scatter is executed sequentially, one subgroup at a
* time. This is needed, when the full collective needs to be specified before
* the collective operation starts up. The algorithm handles all data sizes
* and data types.
*/
OMPI_DECLSPEC int mca_coll_ml_scatter_sequential(
void *sbuf, int scount, struct ompi_datatype_t *sdtype,
void *rbuf, int rcount, struct ompi_datatype_t *rdtype,
int root, struct ompi_communicator_t *comm,
mca_coll_base_module_t *module);
#if 0
int mca_coll_ml_bcast_small_dynamic_root(void *buf, int count, struct ompi_datatype_t *dtype,
int root, struct ompi_communicator_t *comm,
mca_coll_base_module_t *module);
int mca_coll_ml_bcast_small_known_root(void *buf, int count, struct ompi_datatype_t *dtype,
int root, struct ompi_communicator_t *comm,
mca_coll_base_module_t *module);
#endif
/* Topology discovery function */
int mca_coll_ml_fulltree_hierarchy_discovery(mca_coll_ml_module_t *ml_module,
int n_hierarchies);
int mca_coll_ml_allreduce_hierarchy_discovery(mca_coll_ml_module_t *ml_module,
int n_hierarchies);
int mca_coll_ml_fulltree_exclude_basesmsocket_hierarchy_discovery(mca_coll_ml_module_t *ml_module,
int n_hierarchies);
int mca_coll_ml_fulltree_ptp_only_hierarchy_discovery(mca_coll_ml_module_t *ml_module,
int n_hierarchies);
int mca_coll_ml_fulltree_iboffload_only_hierarchy_discovery(mca_coll_ml_module_t *ml_module,
int n_hierarchies);
void mca_coll_ml_allreduce_matrix_init(mca_coll_ml_module_t *ml_module,
const mca_bcol_base_component_2_0_0_t *bcol_component);
static inline int mca_coll_ml_err(const char* fmt, ...)
{
va_list list;
int ret;
va_start(list, fmt);
ret = vfprintf(stderr, fmt, list);
va_end(list);
return ret;
}
#define ML_ERROR(args) \
do { \
mca_coll_ml_err("[%s]%s[%s:%d:%s] COLL-ML ", \
ompi_process_info.nodename, \
OMPI_NAME_PRINT(OMPI_PROC_MY_NAME), \
__FILE__, __LINE__, __func__); \
mca_coll_ml_err args; \
mca_coll_ml_err("\n"); \
} while(0)
#if OPAL_ENABLE_DEBUG
#define ML_VERBOSE(level, args) \
do { \
if(mca_coll_ml_component.verbose >= level) { \
mca_coll_ml_err("[%s]%s[%s:%d:%s] COLL-ML ", \
ompi_process_info.nodename, \
OMPI_NAME_PRINT(OMPI_PROC_MY_NAME), \
__FILE__, __LINE__, __func__); \
mca_coll_ml_err args; \
mca_coll_ml_err("\n"); \
} \
} while(0)
#else
#define ML_VERBOSE(level, args)
#endif
#define IS_BCOL_TYPE_IDENTICAL(bcol1, bcol2) \
( (NULL != bcol1 && NULL != bcol2) && \
( /* chech if the len is the same */ \
(strlen(((mca_base_component_t *)((bcol1)->bcol_component))->mca_component_name) == \
strlen(((mca_base_component_t *)((bcol2)->bcol_component))->mca_component_name)) \
&& /* check if the string are identical */ \
(0 == strncmp(((mca_base_component_t *)((bcol1)->bcol_component))->mca_component_name, \
((mca_base_component_t *)((bcol2)->bcol_component))->mca_component_name, \
strlen(((mca_base_component_t *)((bcol2)->bcol_component))->mca_component_name))) \
) ? true : false)
#define GET_BCOL(module, indx) ((module)->component_pairs[(indx)].bcol_modules[0])
#define GET_BCOL_SYNC_FN(bcol) ((bcol)->filtered_fns_table[DATA_SRC_KNOWN][NON_BLOCKING] \
[BCOL_SYNC][1][0][0])
/* Allocator macros */
#define BUFFER_INDEX(bank,nbuffs,buffer) (bank*nbuffs+buffer)
#define ML_GET_FRAG_SIZE(op, coll) \
((op)->fragment_data.message_descriptor->n_bytes_total - \
(op)->fragment_data.message_descriptor->n_bytes_scheduled < \
(size_t) OP_ML_MODULE((op))->small_message_thresholds[coll] ? \
(op)->fragment_data.message_descriptor->n_bytes_total - \
(op)->fragment_data.message_descriptor->n_bytes_scheduled : \
(size_t) OP_ML_MODULE((op))->small_message_thresholds[coll])
/* Abort mpi process in case of fatal error */
void mca_coll_ml_abort_ml(char *message);
#define ML_SET_VARIABLE_PARAMS_BCAST(op, ml, cnt, datatype, b_desc, \
s_offset, r_offset, frag_len, buf) \
do { \
op->variable_fn_params.sequence_num = \
OPAL_THREAD_ADD32(&((ml)->collective_sequence_num), 1); \
op->variable_fn_params.count = cnt; \
op->variable_fn_params.dtype = datatype; \
op->variable_fn_params.buffer_index = (b_desc)->buffer_index; \
op->variable_fn_params.src_desc = (b_desc); \
op->variable_fn_params.sbuf_offset = s_offset; \
op->variable_fn_params.rbuf_offset = r_offset; \
op->variable_fn_params.frag_size = frag_len; \
op->variable_fn_params.sbuf = buf; \
} while (0)
#define MCA_COLL_ML_OP_BASIC_SETUP(op, total_bytes, offset_into_user_buff, src, dst, collective_schedule) \
do { \
op->coll_schedule = collective_schedule; \
op->process_fn = NULL; \
op->full_message.n_bytes_total = total_bytes; \
op->full_message.n_bytes_delivered = 0; \
op->full_message.n_bytes_scheduled = 0; \
op->full_message.dest_user_addr = dst; \
op->full_message.src_user_addr = src; \
op->full_message.n_active = 0; \
op->full_message.n_bytes_per_proc_total = 0; \
op->full_message.send_count = 0; \
op->full_message.recv_count = 0; \
op->full_message.send_extent = 0; \
op->full_message.recv_extent = 0; \
op->full_message.offset_into_send_buffer = 0; \
op->full_message.offset_into_recv_buffer = 0; \
op->full_message.send_data_type = 0; \
op->full_message.recv_data_type = 0; \
op->full_message.fragment_launcher = 0; \
op->sequential_routine.current_active_bcol_fn = 0; \
op->sequential_routine.current_bcol_status = SEQ_TASK_NOT_STARTED; \
\
op->fragment_data.offset_into_user_buffer = offset_into_user_buff; \
/* Pasha, is it constant ? what to put here */ \
op->fragment_data.fragment_size = total_bytes; \
op->fragment_data.message_descriptor = &op->full_message; \
op->fragment_data.current_coll_op = -1; \
} while (0)
/* This routine re-orders and packs user data. The assumption is that
* there is per-process data, the amount of data is the same for all * ranks,
* and the user data is contigous.
*/
int mca_coll_ml_pack_reorder_contiguous_data(
mca_coll_ml_collective_operation_progress_t *coll_op);
/* This routine re-orders and packs user data. The assumption is that
* there is per-process data, the amount of data is the same for all * ranks,
* and the user data is noncontigous.
*/
int mca_coll_ml_pack_reorder_noncontiguous_data(
mca_coll_ml_collective_operation_progress_t *coll_op);
END_C_DECLS
#endif /* MCA_COLL_ML_ML_H */
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