Replicating Memory Behavior for Performance Skeletons

Replicating Memory Behavior for Performance Skeletons By Aditya Toomula PC-Doctor Inc. Reno, NV Jaspal Subhlok University of Houston Houston, TX

Resource Selection for Grid Applications Model Data GUI Sim 1 Pre Stream Application ? where is the best performance Network

Motivation • Estimating performance of an application in dynamically changing grid environment. - Estimation based on generic system probes (like NWS) is expensive and error prone. • Estimating performance for micro-architectural simulations. - Executing full application is prohibitively expensive.

Our Approach Application Model Data Network GUI Sim 1 Pre Stream PREDICT APPLICATION PERFORMANCE BY RUNNING A SMALL PROGRAM REPRESENTATIVE OF ACTUAL DISTRIBUTED APPLICATION

Performance Skeletons • A synthetically generated short running program. • Skeleton reflects the performance of the application it represents in any execution scenario. -E.g. skeleton execution time is always 1/1000th of application execution time • An application and its skeleton should have similar execution activities for the above to be true - Communication activity - CPU activity - Memory access pattern

Memory Skeleton Given an executable application, construct a short running skeleton program whose memory access behavior is representative of the application. An application and its memory skeleton should have similar cache performance for any cache hierarchy. Solution approach: create a program that recreates memory accesses in a sequence of representative slices of the executing program

Challenges in creating a Memory Skeleton • Memory trace is prohibitively large even for a few minutes of execution • solution approach : sampling and compression – lossy if necessary • Recreating memory accesses from a trace is difficult • cache is corrupted by management code • recreation has substantial overhead – several instructions have to be executed to issue a memory access request • solution approach: avoid cache corruption and allow reordering that would minimize overhead per access

Memory Access Behavior of Applications Two types of locality. • Spatial Locality - if one memory location is accessed then nearby memory locations are also likely to be accessed. • Temporal Locality - if something is accessed once, it is likely to be accessed again soon. These locality principles should be preserved in the memory skeleton

Model Data GUI Sim 1 Pre Stream CREATE SKELETON Automatic Skeleton Construction Framework Model Data GUI Sim 1 Pre Stream Skeleton Application Generate memory skeleton. Collect data address trace samples of the application. Summarize the trace samples.

Address Trace Collection • Link the application executable with Valgrind Tool - Generates address trace of the application. - Access to source code not required. • Issues - Unacceptable level of storage space and time overhead. Hence, sampling of address trace must be done during trace collection itself – collection of full traces of applications is prohibitively expensive.

Address Trace Collection (Contd…) • TraceSampling - Divide the trace into trace slices: set of consecutive memory references. - tool can be periodically switched on and off to capture these slices. - slices can be collected at random or uniform intervals. Slice size should be at least one order of magnitude greater than the largest cache expected, in order to capture the temporal locality.

Trace Compaction • Recorded trace is still large and expensive to recreate • Compress the trace using the following two ideas - Exact address in a trace is not critical – a nearby address will work – may affect spatial locality - Slight reordering of address trace does not affect performance. – may affect temporal locality This is lossy compression but impact on locality can be reduced to be negligible

Trace Compaction (contd…) • Divide the address space into lines of size ~ typical cache line - record only the line number, not full address. • Impact on spatial locality should be minimal • Divide the temporal sequence of line numbers into clusters.Reordering within a cluster is allowed. • Cluster size should be much smaller than the smallest expected cache size so temporal locality is not affected by reordering

Memory Skeleton Generation Create a C-program that synthetically generates a sequence of memory references recorded in the previous step. Challenges • Minimizing extraneous address references. - Any executing program has memory accesses of its own. - Generate a loop structure for each cluster. - Reading linenumber-frequency pairs once leads to series of actual memory references from the trace without intervening address reads.

Skeleton Generation (contd…) • Eliminating cache corruption - Reading trace data from disk impacts the memory simulation. • Use 2nd machine, read data on one machine and send it through sockets to the other machine where simulation is done. • Socket buffer is kept to a very small size • Also reduces overhead on the main simulation machine.

Skeleton Generation (contd…) • Allocating memory - The regions of virtual memory that will actually be used are not known prior to simulation Dynamic block allocation Substantial size block of memory is allocated when an address reference is made to a location that is not allocated. Maintain Sparse Index Tableto access the blocks.

Experiments and Results Skeletons constructed for Class-W NAS serial benchmarks (and Class-A IS benchmark). Experiments conducted on Intel Xeon dual CPU 1.7 GHz machines with 256KB 8-way set associative L2 cache and 64 byte lines, running Linux 2.4.7. Objectives: • Prediction of cache miss ratio of corresponding applications. • Predictions across different memory hierarchies. Trace slices picked uniformly throughout the trace for all the experiments.

Prediction of Cache Miss Ratios Trace Sampling ratio = 10% Trace slice size > 10 million references No. of slices picked > 10 Average error < 5% Comparison of data cache miss rate of benchmarks and corresponding memory skeletons. IS application is an exception.

Impact of trace slice selection • Traces for IS, BT and MG benchmarks divided into 100 uniform slices. • 10 different versions of skeletons generated each using different sets of 10 uniformly spaced trace slices. - MG and BT have similar cache miss rates in all cases. - IS has significant variation in cache miss prediction with different sets of slices. Reason: IS execution goes through different phases with different memory access behavior unlike CG and BT. Data cache miss rates for different sets of trace slices in skeletons. Actual Data cache miss rates of IS – 3.9% BT – 2.76% MG – 1.57%

Impact of trace slice selection (contd…) - Greater the number of trace slices in an application trace, smaller the size of each trace slice. True cache miss ratio - Having large number of slices in the trace captures the multiphase behavior of applications. Data cache miss rates of IS skeletons with different sets of slices and for different number of slices.

Impact of trace slice size - The cache miss ratio prediction error increases rapidly when the slice sizes are reduced below a certain point. Error in cache miss prediction with memory skeletons for different trace slice sizes for MG.

Prediction across hardware platforms Cache miss ratios were predicted fairly accurately with error < 5% across all machines. Cache miss comparison of CG benchmark and its skeleton across different memory hierarchies.

Conclusion and Discussions • Presents a methodology to build memory skeletons for prediction of application cache miss ratio across hardware platforms. • A step towards building good performance skeletons • Extends our group’s previous work on skeletons to memory characteristics. Major Contribution • Low overhead generation of memory accesses from a trace.

Conclusion and Discussions (Contd…) Limitations • Instruction references • Space and time Overhead • Timing accuracy • Integration with Communication and CPU events

Replicating Memory Behavior for Performance Skeletons