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Posts Tagged ‘cosmic rays’

Source code will soon need to be radiation hardened

May 29th, 2014 2 comments

I think I have discovered a new kind of program testing that may soon need to be performed by anybody wanting to create ultra-reliable software.

A previous post discussed the compiler related work being done to reduce the probability that a random bit-flip in the memory used by an executing program will result in a change in behavior. At the moment 4G of ram is expected to experience 1 bit-flip every 33 hours due to cosmic rays and the rate of occurrence is likely to increase.

Random corrupts on communications links are protected by various kinds of CRC checks. But these checks don’t catch every corruption, some get through.

Research by Artem Dinaburg looked for, and found, occurrences of bit-flips in domain names appearing within HTTP requests, e.g., a page from the domain ikamai.net being requested rather than from akamai.net. A subsequent analysis of DNS queries to VERISIGN’S name servers found “… that bit-level errors in the network are relatively rare and occur at an expected rate.” (the 2.10^{-9} bit error rate was thought to occur inside routers and switches).

Javascript is the web scripting language supported by all the major web browsers and the source code of JavaScript programs is transmitted, along with the HTML, for requested web pages. The amount of JavaScript source can dwarf the amount of HTML in a web page; measurements from four years ago show users of Facebook, Google maps and gmail receiving 2M bytes of Javascript source when visiting those sites.

If all the checksums involved in TCP/IP transmission are enabled the theoretical error rate is 1 in 10^17 bits. Which for 1 billion users visiting Facebook on average once per day and downloading 2M of Javascript per visit is an expected bit flip rate of once every 5 days somewhere in the world; not really something to worry about.

There is plenty of evidence that the actual error rate is much higher (because, for instance, some checksums are not always enabled; see papers linked to above). How much worse does the error rate have to get before developers need to start checking that a single bit-flip to the source of their Javascript program does not result in something nasty happening?

What we really need is a way of automatically radiation hardening source code.

Unreliable cpus and memory: The end result of Moore’s law?

December 13th, 2013 2 comments

Where is the evolution of commodity cpu and memory chips going to take its customers? I think the answer is cheap and unreliable products (just like many household appliances are priced low and have a short expected lifetime).

We have had the manufacturer-customer win-win phase of Moore’s law and I think we are now entering the win-loose phase.

The reason chip manufacturers, such as Intel, invest so heavily on continually shrinking dies is the same reason all companies invest, they expect to get a good return on their investment. The cost of processing the wafer from which individual chips are cut is more or less constant, reducing the size of a chip enables more to fitted on the same wafer, giving more product to sell for more or less the same wafer processing cost.

The fact that dies with smaller feature sizes have reduce power consumption and can run at faster clock speeds (up until around 10 years ago) is a secondary benefit to manufacturers (it created a reason for customers to replace what they already owned with a newer product); chip manufacturers would still have gone down the die shrink path if these secondary benefits had not existed, but perhaps at a slower rate. Customers saw, or were marketed, this strinkage story as one of product improvement for their benefit rather than as one of unit cost reduction for Intel’s benefit (Intel is the end-customer facing company that pumped billions into marketing).

Until recently both manufacturer and customer have benefited from die shrinks through faster cpus/lower power consumption and lower unit cost.

A problem that was rarely encountered outside of science fiction a few decades ago is now regularly encountered by all owners of modern computers, cosmic rays (plus more local source of ‘rays’) altering the behavior of running programs (4 GB of RAM is likely to experience a single bit-flip once every 33 hours of operation). As die shrink continues this problem will get worse. Another problem with ever smaller transistors is their decreasing mean time to failure (very technical details); we have seen expected chip lifetimes drop from 10 years to 7 and now less and decreasing.

Decreasing chip lifetimes is actually good for the manufacturer, it creates a reason for customers to buy a new product. Buying a new computer every 2-3 years has been accepted practice for many years (because the new ones were much better). Are we, the customer, in danger of being led to continue with this ‘accepted practice’ (because computers reliability is poor)?

Surely it is to the customer’s advantage to not buy devices that contain chips with even smaller features? Is it only the manufacturer that will obtain a worthwhile benefit from future die shrinks?

Impact of compiler optimization level on recovery from a hardware error

September 24th, 2012 No comments

I have previously written about cosmic-ray induced faults in cpus and some of the compiler research being done to recover from such hardware faults. If your program is executing in an environment where radiation may cause hardware bit-flips to occur and you don’t have access to a research compiler providing some level of recovery, is it better to compile with high or low levels of optimization?

Short answer: Using gcc with optimization options O2 or O3 reduces the probability that a bit-flip will change the external behavior of a program, compared to option O0.

The longer answer is below as another draft section from my book Empirical software engineering with R book. As always comments welcome.

Software masking of hardware faults

Like all hardware cpus are subject to intermittent faults, these faults may flip the value of a bit in a program visible register, a bit in an executable instruction or some internal processor state (causes include cosmic rays <book ???> and electrical wear of the material from which circuits are built).

If a bit-flip randomly occurs at some point during a program’s execution, is it less likely to effect external program behavior when the code has been built with high levels of compiler optimization or built with optimization disabled or at a low level?

  1. many optimizations reduce the number of instructions executed (reducing execution time reduces the probability of encountering a bit-flip) and makes more efficient use of registers (e.g., keeping needed values in registers over longer periods of time and reducing the time intervals when a registers is not in use; which increases the probability that a bit-flip will propagate to external behavior),
  2. fewer compiler optimizations is likely to result in an increased number of instructions executed (increasing the probability that a bit-flip will occur during program execution) and results in lower register usage efficiency (e.g., longer periods of time between the last use of register contents and a new value being loaded; increasing the probability that a bit-flip will modify a value that is never used again).

A study by Cook and Zilles <book Cook_08> flipped one bit in an executing program (100 evenly distributed points in the program were chosen and 100 instructions from each of those points were used as fault injection points, giving a total of 10,000 individual tests to be run) and monitored the impact on subsequent execution; this process was repeated between 32 and 244 times for each injection point, once for every bit in the 32-bit instruction, zero, one or two of its 64-bit input registers and one possible 64-bit output result register (i.e., the bit-flip only involved the current instruction and its input/output, not the contents of any other register or main memory).

The monitoring process consisted of two parallel executions containing the modified processor state and the unmodified processor state. The behavior of the two executions were compared to see if the fault did not propagate (a passing trial, e.g., a bit-wise AND of a register with 0xff when a bit-flip has been applied to one of the top 24 bits of the register, also the values in a branch not-equal are usually not-equal and a bit-flip is likely to maintain that state), caused a failure (either due to a compulsory event caused by a hardware trap such as an invalid instruction or an incorrectly aligned memory access, or what was called an error model event such as a control flow mismatch or writing a different value to storage), or is inconclusive (pass/fail did not occur within 10,000 executed instructions of the fault injection point).

Data

The available data consists of the normalised number of program executions having one of the behaviors pass, fail (compulsory), fail (error model, broken down into control flow and store related cases) or inconclusive for nine programs from the SPEC2000 integer benchmark compiled using gcc version 4.0.2 and the DEC C compiler (henceforth called osf) host compiler which was native to the platform. Every program was compiled and executed with each of the gcc optimization options O0, O2 and O3, for osf the O4 option was used.

There are nine measurements for each of the nine SPEC programs, repeated at 3 optimization levels for gcc and once for osf (the osf data is not analysed here).

Is the data believable?

Injecting bit-flip faults at all points in a program and monitoring for subsequence changes in external behavior would be an enormous task, sets of 100 instructions starting from 100 locations appears to be an unbiased sample.

The error model used checks for changes of control flow and different values being stored to memory, it does not check for actual changes in external program behavior. This model biases the measurements in favour of more bit-flips being counted as generating an error than would occur in practice.

Predictions made in advance

Does compiler optimization level change the probability that a bit-flip will cause a change in external program behavior?

No hypothesis is proposed suggesting that compiler optimization level will increase, decrease or have no effect on the probability of a bit-flip effecting external program behavior.

Applicable techniques

The data was originally a count of the number of instances and this has been normalised to a value between 0 and 100. The same number of programs were executed at all optimization levels.

Non-parametric techniques have to be used because nothing is known about the distribution of values.

The [Wilcoxon signed-rank test] is a test for two dependent samples while the [Mann-Whitney U test] is a test for two independent samples. To what extent does running gcc at different optimization levels make it a different compiler? Given that we are testing for the possibility that compiler optimizations do effect the results then it is necessary to treat the samples as being independent.

The function wilcox.test will perform a Mann-Whitney test if the parameter paired is FALSE (the default) and will generate a confidence interval if the parameter conf.int is TRUE (the default is FALSE).

Results

The Mann-Whitney test of the various measurements obtained using the O2 and O3 options finds no worthwhile difference between them. There are interesting differences in the values obtained using both of two options and the O0 option, as follows:

Pass
Comparing percentage of pass behaviors for O0 and O2 we see: p-values = 0.005 and 0.005
> wilcox.test(gcc.o0$pass.masked, gcc.o2$pass.masked, conf.int=TRUE)
        Wilcoxon rank sum test with continuity correction

data:  gcc.o0$pass.masked and gcc.o2$pass.masked
W = 8, p-value = 0.004697
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
 -15.449995  -2.020001
sample estimates:
difference in location
             -7.480088

The wilcox.test function returns an estimate of the difference between the two means and a negative value occurs if the second argument (the higher optimization level in this case) has a greater mean than the first argument (which is always the O0 option in these results).

O0/O3 95% confidence interval: -15.579959 -1.909965, mean: -4.780058

Fail (compulsory)
  • Memory protection fault: pvalues = 0.002 and 0.005

    O0/O2 95%: 2.1 7.5, mean: 4.9

    O0/O3 95%: 1.9 7.3, mean: 4.1
  • Invalid instruction: p-values = 0.045 and 0.053

    O0/O2 95%: -8.0e-01 -4.9e-08, mean: -0.5

    O0/O3 95%: -6.4e-01 5.1e-06, mean: -0.3
Fail (error model)
  • Control flow: p-values = 0.0008 and 0.002

    O0/O2 95%: -10.8 -3.8, mean: -7.0

    O0/O3 95%: -10.5 -3.7, mean: -6.8
  • Store related: p-values = 0.002 and 0.003

    O0/O2 95%: 4.78 22.02, mean: 11.24

    O0/O3 95%: 4.93 18.78, mean: 10.51

Discussion

O2 and O3 options differences
The issue of optimization performance differences between the gcc O2 and O3 options is covered in [another section] of this book. That analysis found that the only difference between the two options was an increase in code size with O3, probably because of function inlining.

If there is no significant difference in the code generated by the O2/O3 options then no difference in bit-flip behavior is expected, and none was seen.

Changes in failure rates
The results show a decrease in store related errors at high optimization levels and an increase in control flow related errors. Why is this?

Optimizing register usage is a very important optimization and one of its consequences is a reduction in the number of stores to memory and loads having a corrupted address triggering a protection fault . A reduction in the number of memory related instructions executed will feed through into a reduction in the number of failures classified as store related or memory protection faults and this is seen in the shift in mean value of fails between high and low optimization levels.

Keeping a value containing an injected bit-flip in a register for a longer period of program execution (rather than being stored to memory and loaded back later) provides the opportunity for it to work its way through subsequent instructions and either disappear (being counted as a pass) or cause a control flow failure. It is likely that some of the change stored values flagged by the error model do not an impact on external program behavior and the pass count at low optimization levels is lower than would occur in practice.

Changes in pass rate
The additional optimizations of register usage enabled by the O2/O3 options reduces memory accesses which leads to a reduction in memory protection errors, an unrecoverable fault under all circumstances. The numbers suggest that while this is a major factor in the increased pass rate, contributions are made by other sources, e.g., bit-flips not contributing to the result calculated by an instruction; the data is not sufficiently detailed to enable a reliable estimate of this contribution to be made.

The pass rate is likely to be an underestimate because the error model classifies storing a different value as a failure, however the different value might not result in a change of external program behavior, e.g., the value stored might never be used again. Some of the stores classified as errors for the O0 option have no lasting affect in practice (and being kept in registers for O2/O3 had the opportunity to be masked out). No data is available for enable an estimate to be made for the percentage of these bit-flips have no lasting affect.

The average pass rate for gcc using the O0 option was 28% and this increased to around 36% when the O2/O3 options were used.

Other processors
How likely is it that the bit-flip pass rates seen on the Alpha (average of 36% for high optimization, 28% for low) would also occur on other processors?

The Alpha registers contain 64-bit and instructions operating on just 32 or 16 of those bits are supported. A study by Loh <book Loh_0?> of the Alpha running SPEC2000 programs found that 48% of executed instructions operated on 64-bits, 24% on 32-bits and 28% on 16-bits. Based on these numbers 33% of single bit-flips of a 64-bit register would not be expected to affect the result of an instruction (the table below gives the percentages measured by Cook et al).

Table 1. Percentage of injected faults having behavior pass as a function of kind of injection site at various gcc optimization levels. Data from Cook <book Cook_08>.
injection site O3 O2 O0
instruction
28.2
29.2
21.3
input register1
49.0
50.0
40.5
input register2
26.5
28.5
17.9
output register
39.6
41.9
34.7

A lot of software is based on using 32-bit integers and it might be expected that a much lower percentage of register bit-flips would result in pass behavior, compared to a 64-bit processor (where most operations that access 64 bits involve addresses). However, 32-bit processors usually contain instructions for operating on just 8-bits of a register <book ???> and use of these instructions creates more opportunities for bit-flips to have no lasting consequences.

The measurements of Cook and Zilles have shown how interrelated instruction set interactions are. Without measurements from 32-bit processors it is not possible to estimate the extent to which bit-flips will impact external program behavior.

Conclusion

Compiling source using high levels of compiler optimization reduces the likelihood that a randomly occurring bit-flip during program execution will effect external program behavior. For processors that perform memory access checks the largest decrease in bit-flip induced faults is a reduction in memory protection faults.

Optimization generally reduces the number of instructions executed by a program, reducing the probability that a bit-flip will occur between the start and end of execution, further increasing the advantage of optimized code over non-optimized.

Compiling to reduce the impact of soft errors on program output

November 7th, 2011 No comments

Optimizing compilers have traditionally made code faster and smaller (sometimes a choice has to be made between faster/larger and slower/smaller). The huge growth in the use of battery power devices has created a new attribute for writers of optimizers to target, finding code sequences that minimise power consumption (I previously listed this as a major growth area in the next decade). Radiation (e.g., from cosmic rays) can cause a memory or processor bit to flip, known as a soft error, and I have recently been reading about how code can be optimized to reduce the probability that soft errors will alter the external behavior of a running program.

The soft error rate is usually quoted in FITs (Failure in Time), with 1 FIT corresponding to 1 error per 10^9 hours per megabit, or 10^-15 errors per bit-hour. A PC with 4 GB of DRAM (say 1000 FIT/Mb which increases with altitude and is 10 times greater in Denver Colorado) has a MTBF (mean time between failure) of 1000 * 10^-15 * 4.096 * 10^9 * 8 = 3.2 10^-2 hours, around once every 33 hours. Calculating the FIT for processors is complicated.

Uncorrected soft errors place a limit on the maximum number of computing nodes that can be usefully used by one application. At around 50,000 nodes a system will be spending half its time saving checkpoints and restarting from previous checkpoints after an error occurs.

Why not rely on error correcting memory? Super computers containing terrabytes are built containing error correcting memory, but this does not make the problem go away, it ‘only’ reduces it by around two orders of magnitude. Builders of commodity processors don’t use much error correction circuitry because it would increase costs/power consumption/etc for an increased level of reliability that the commodity market is not interested in; vendors of high-end processors add significant amounts of error correction circuitry.

Most of the compiler research I am aware of involves soft errors occurring on the processor and this topic is discussed below; there has been some work on assigning variables deemed to be critical to a subset of memory that is protected with error correcting hardware. Pointers to other compiler research involving memory soft errors welcome.

A commonly used technique for handling hardware faults is redundancy, usually redundant hardware (e.g., three processors performing the same calculating and a majority vote used to decide which of the outputs to accept). Software only approaches include the compiler generating two or more independent machine code sequences for each source code sequence whose computed values are compared at various check points and running multiple copies of a program in different threads and comparing outputs. The
Shoestring compiler (based on llvm) takes a lightweight approach to redundancy by not duplicating those code sequences that are less affected by register bit flips (e.g., the value obtained from a bitwise AND that extracts 8 bits from a 32-bit register is 75% less likely to deliver an incorrect result than an operation that depends on all 32 bits).

The reliability of single ‘thread’ generated code can be improved by optimizing register lifetimes for this purpose. A value is loaded into a register and sometime later it is used one or more times. A soft error corrupting register contents after the last use of the value it contains has no impact on program execution, the soft error has to occur between the load and last use of the value for it to possibly influence program output. One group of researchers modified a compiler (Trimaran) to order register usage such that the average interval between load and last usage was reduced by 10%, compared to the default behavior.

Developers don’t have to wait for compiler or hardware support, they can improve reliability by using algorithms that are robust in the presence of ‘faulty’ hardware. For instance, the traditional algorithms for two-process mutual exclusion are not fault tolerant; a fault tolerant mutual exclusion algorithm using 2f+1 variables, where a single fault may occur in up to f variables is available.

CPUs also exhibit hardware faults

October 31st, 2009 No comments

The cpu is the one element of a computing platform that people rarely treat as a source of error caused by physically malfunction, i.e., randomly flipping a bit in a register or instruction pipeline. I once worked on a compiler for the Mototola 88000 using a test platform that contained alpha silicon (i.e., not yet saleable components where some of the instructions were known not to work; the generated assembler code was piped through a sed script that mapped these instructions into an alternative instruction sequence that did work) and the cpus in a few of the hardware updates turned out to be temperature sensitive; some of the instructions changed their behavior when they got too hot. People who write compilers using alpha silicon learn to expect this sort of thing.

Quite a bit has been published on faults in other hardware components. Some of the best recent empirical hardware fault data and analysis I have seen is that published by Google engineers on hard disc and dram memory fault occurrences in their server farms. They might have a problem publishing such results for the cpus they use because these commodity items generally don’t have the ability to report any detailed fault data, they just die or one of the programs being executed crashes.

As device fabrication continues to shrink erroneous behavior caused by cosmic ray impact will become more and more common. Housing a computer farm at a high altitude might not be a good idea (at 7500 ft cosmic ray-induced neutrons that can lead to soft errors are 6.4 times more common than at sea level).

IBM’s Power4 chip (“Power4 System Design for High Reliability” by Bossen, Tendler and Reick) is one of the few that provides error checking of cache contents, while IBM’s System z9 is one of the very few that provide parity checking on the cpu registers (“Enhanced I/O subsystem recovery and availability on the IBM System z9″ by Oakes et al).

One solution to the problem of unreliable cpu behavior is for the compiler to insert consistency checks into the generated code. Two such checking methods are:

  • ‘Signature Analysis’ which performs consistency checks between signatures calculated at compile time and runtime. A signature is associated with every basic block with the current signature being derived from the execution history. This technique can detect spurious changes to the flow of control caused by a hardware glitch.
  • ‘Error Detection by Duplicated Instructions’ generates code which duplicates the behavior of some instruction sequence and compares the result calculated by both sequences, i.e., a source language construct is executed twice and an error raised if the results are different. The parallel instruction sequences use different sets of registers on the same cpu and ideally the instructions are scheduled to exploit instruction level parallelism

At the moment cosmic-ray induced hardware faults are probably very small change compared to faults in the code. Will code quality increase to the point where cosmic-ray faults become an issue or will devices get so small that they have to be lead lined to prevent background radiation corrupting them? Let the race begin.