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Notes on the mystery of hardware cache performance counters

Posted by John D. McCalpin, Ph.D. on July 14, 2013

In response to a question on the PAPI mailing list, I scribbled some notes to try to help users understand the complexity of hardware performance counters for cache accesses and cache misses, and thought they might be helpful here….

For any interpretation of specific hardware performance counter events, it is absolutely essential to precisely specify the processor that you are using.

Cautionary Notes

Although it may not make a lot of sense, the meanings of “cache miss” and “cache access” are almost always quite different across different vendors’ CPUs, and can be quite different for different CPUs from the same vendor. It is actually rather *uncommon* for L1 cache misses to match L2 cache accesses, for a variety of reasons that are difficult to summarize concisely.

Some examples of behavior that could make the L1 miss counter larger than the L2 access counter:

  • If an instruction fetch misses in the L1 Icache, the fetch may be retried several times before the instructions have been returned to the L1 Icache. The L1 Icache miss event might be incremented every time the fetch is attempted, while the L2 cache access counter may only be incremented on the initial fetch.
  • L1 caches (both data and instruction) typically have hardware prefetch engines. The L1 Icache miss counter may only be incremented when the instruction fetcher requests data that is not found in the L1 Icache, while the L2 cache access counter may be incremented every time the L2 receives either an L1 Icache miss or an L1 Icache prefetch.
  • The processor may attempt multiple instruction fetches of different addresses in the same cache line. The L1 Icache miss event might be incremented on each of these fetch attempts, while the L2 cache access counter might only be incremented once for the cache line request.
  • The processor may be fetching data that is not allowed to be cached in the L2 cache, such as ROM-resident code. It may not be allowed in the L1 Instruction cache either, so every instruction fetch would miss in the L1 cache (because it is not allowed to be there), then bypass access to the L2 cache (because it is not allowed to be there), then get retrieved directly from memory. (I don’t know of any specific processors that work this way, but it is certainly plausible.)

An example of behavior that could make the L1 miss counter smaller than the L2 access counter: (this is a very common scenario)

  • The L1 instruction cache miss counter might be incremented only once when an instruction fetch misses in the L1 Icache, while the L2 cache might be accessed repeatedly until the data actually arrives in the L2. This is especially common in the case of L2 cache misses — the L1 Icache miss might request data from the L2 dozens of times before it finally arrives from memory.

A Recommended Procedure

Given the many possible detailed meanings of such counters, the procedure I use to understand the counter events is:

  1. Identify the processor in detail.
    This includes vendor, family, model, and stepping.
  2. Determine the precise mapping of PAPI events to underlying hardware events.
    (This is irritatingly difficult on Linux systems that use the “perf-events” subsystem — that is a long topic in itself.)
  3. Look up the detailed descriptions of the hardware events in the vendor processor documentation.
    For AMD, this is the relevant “BIOS and Kernel Developers Guide” for the processor family.
    For Intel, this Volume 3 of the “Intel 64 and IA-32 Architecture Software Developer’s Guide”.
  4. Check the vendor’s published processor errata to see if there are known bugs associated with the counter events in question.
    For AMD these documents are titled “Revision Guide for the AMD Family [nn] Processors”.
    For Intel these documents are usually given a title including the words “Specification Update”.
  5. Using knowledge of the cache sizes and associativities, build a simple test code whose behavior should be predictable by simple paper-and-pencil analysis.
    The STREAM Benchmark is an example of a code whose data access patterns and floating point operation counts are easy to determine and easy to modify.
  6. Compare the observed performance counter results for the simple test case with the expected results and try to work out a model that bridges between the two.
    The examples of different ways to count given at the beginning of this note should be very helpful in attempting to construct a model.
  7. Decide which counters are “close enough” to be helpful, and which counters cannot be reliably mapped to performance characteristics of interest.

An example of a counter that (probably) cannot be made useful

As an example of the final case — counters that cannot be reliably mapped to performance characteristics of interest — consider the floating point instruction counters on the Intel “Sandy Bridge” processor series. These counters are incremented on instruction *issue*, not on instruction *execution* or instruction *retirement*. If the inputs to the instruction are not “ready” when the instruction is *issued*, the instruction issue will be rejected and the instruction will be re-issued later, and may be re-issued many times before it is finally able to execute. The most common cause for input arguments to not be “ready” is that they are coming from memory and have not arrived in processor registers yet (either explicit load instructions putting data in registers or implicit register loads via memory arguments to the floating-point arithmetic instruction itself).

For a workload with a very low cache miss rate (e.g., DGEMM), the “overcounting” of FP instruction issues relative to the more interesting FP instruction execution or retirement can be as low as a few percent. For a workload with a high cache miss rate (e.g., STREAM), the “overcounting” of FP instructions can be a factor of 4 to 6 (perhaps worse), depending on how many cores are in use and whether the memory accesses are fully localized (on multi-chip platforms). In the absence of detailed information about the processor’s internal algorithm for retrying operations, it seems unlikely that this large overcount can be “corrected” to get an accurate estimate of the number of floating-point operations actually executed or retired. The amount of over-counting will likely depend on at least the following factors:

  • the instruction retry rate (which may depend on how many instructions are available for attempted issue in the processor’s reorder buffer, including whether or not HyperThreading is enabled),
  • the instantaneous frequency of the processor (which can vary from 1.2 GHz to 3.5 GHz on the Xeon E5-2670 “Sandy Bridge” processors in the TACC “Stampede” system),
  • the detailed breakdown of latency for the individual loads (i.e., the average latency may not be good enough if the retry rate is not fixed),
  • the effectiveness of the hardware prefetchers at getting the data into the data before it is needed (which, in turn, is a function of the number of data streams, the locality of the streams, the contention at the memory controllers)

There are likely other applicable factors as well — for example the Intel “Sandy Bridge” processors support several mechanisms that allow the power management unit to bias behavior related to the trade-off of performance vs power consumption. One mechanism is referred to as the “performance and energy bias hint”, and is described as as a “hint to guide the hardware heuristic of power management features to favor increasing dynamic performance or conserve energy consumption” (Intel 64 and IA-32 Architectures Software Developer’s Manual: Volume 3, Section 14.3.4, Document 325384-047US, June 2013). Another mechanism (apparently only applicable to “Sandy Bridge” systems with integrated graphics units) is a pair of “policy” registers (MSR_PP0_POLICY and MSR_PP1_POLICY) that define the relative priority of the processor cores and the graphics unit in dividing up the chip’s power budget. The specific mechanisms by which these features work, and the detailed algorithms used to control those mechanisms, are not publicly disclosed — but it seems likely that at least some of the mechanisms involved may impact the floating-point instruction retry rate.

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