1. A Map-Reduce System with an Alternate API for Multi-Core Environments
Presented by Wei Jiang
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2. Outline Background MapReduce Generalized Reduction
System Design and Implementation
Experiments
Related Work
Conclusions
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3. Background
We have evaluated FREERIDE and Hadoop MapReduce based on a set of applications
Phoenix is one of the implementations of MapReduce for shared-memory systems, written in C, of small code size
We also want to make FREERIDE smaller and light-weighted
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4. Google’s MapReduce Engine
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5. Phoenix implementation
It is based on the same principles but targets shared-memory systems
Consists of a simple API that is visible to application programmers
An efficient runtime that handles parallelization, resource management, and fault recovery
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6. Phoenix runtime
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7. Generalized Reduction
Processing structures
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8. A Case Study: Apriori
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9. A Case Study: Apriori
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10. System Design and Implementation
Basic dataflow of MATE (MapReduce with AlternaTE API)
Data structures to communicate between the user code and the runtime
Three sets of functions in MATE
Example, how to write a user application
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11. MATE runtime dataflow Basic one-stage dataflow April 27, 201911

12. Data structures-(1) scheduler_args_t: Basic fields Field Description
Input_data
Input data pointer
Data_size
Input dataset size
Data_type
Input data type
Stage_num
Computation-Stage number
Splitter
Pointer to Splitter function
Reduction
Pointer to Reduction function
Finalize
Pointer to Finalize function
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13. Data structures-(2)
scheduler_args_t: Optional fields for performance tuning
Field
Description
Unit_size
# of bytes for one element
L1_cache_size
# of bytes for L1 data cache size
Model
Shared-memory parallelization model
Num_reduction_workers
Max # of threads for reduction workers(threads)
Num_procs
Max # of processor cores used
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14. Functions-(1) Transparent to users Function Description R/O
static inline void * schedule_tasks(thread_wrapper_arg_t *) R
static void * combination_worker(void *)
static int array_splitter(void *, int, reduction_args_t *)
void clone_reduction_object(int num)
static inline int isCpuAvailable(unsigned long, int)
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15. Functions-(2) APIs provided by the runtime Function Description R/O
int mate_init(scheudler_args_t * args) R
int mate_scheduler(void * args)
int mate_finalize(void * args) O
void reduction_object_pre_init()
int reduction_object_alloc(int size)—return the object id
void reduction_object_post_init()
void accumulate/maximal/minimal(int id, int offset, void * value)
void reuse_reduction_object()
void * get_intermediate_result(int iter, int id, int offset)
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16. Functions-(3) APIs defined by the user Function Decription R/O
int (*splitter_t)(void *, int, reduction_args_t *) O
void (*reduction_t)(reduction_args_t *) R
Void (*combination_t)(void*)
void (*finalize_t)(void *)
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17. Implementation Considerations
Data partitioning: dynamically assigns splits to worker threads
Buffer management: two temporary buffers, one for reduction objects, the other for combination results
Fault tolerance: re-executes failed tasks; checkingpoint may be a better solution
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18. What is in the user code ?
Implements necessary functions such as reduction, splitter, finalize, and etc.
Generates the input dataset
Setups the fields in scheduler_args_t
Initializes the middleware and declare reduction object(s)
Executes reduction tasks by calling mate_scheduler(one or more passes)
Maybe does some finalizing work
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19. K-means user code int main (int argc, char **argv){ parse_args();
generate_points();
generate_means();
mate_init(&scheduler_args_t);
reduction_object_pre_init();
while(needed) reduction_object_alloc(size);
reduction_object_post_init();
while(not finished) {
mate_scheduler();
update_means();
reuse_reduction_object();
process_next_iteration(); }
mate_finalize();
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20. Experiments: K-means
K-means: 400MB, 3-dim points, k = 100 on one WCI node with 8 cores
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21. Experiments: K-means
K-means: 400MB, 3-dim points, k = 100 on one AMD node with 16 cores
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22. Experiments: PCA PCA: 8000 * 1024 matrix, on one WCI node with 8 cores
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23. Experiments: PCA
PCA: 8000 * 1024 matrix, on one AMD node with 16 cores
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24. Experiments: Apriori
Apriori: 1,000,000 transactions, 3% support, on one WCI node with 8 cores
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25. Experiments: Apriori
Apriori: 1,000,000 transactions, 3% support, on one AMD node with 16 cores
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26. Related Work Improves MapReduce’s API or implementations
Evaluates MapReduce across different platforms and application domains
Acadamia: CGL-MapReduce, Mars, MITHRA, Phoenix, Disco…
Industry: Facebook (Hive), Yahoo! (Pig Latin, Map-Reduce-Merge), Google (Sawzall), Microsoft (Dryad)
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27. Conclusions MapReduce is simple and robust in expressing parallelism
Two-stage computation style may cause
performance losses for some subclasses of applications in data-intensive computing
MATE provides an alternate API that is based on generalized reduction
This variation can reduce overheads of data management and communication between Map and Reduce
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28. Questions?
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