Unified Architectural Support for Soft-Error Protection or Software Bug Detection
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1 Unified Architectural Support for Soft-Error Protection or Software Bug Detection Martin Dimitrov and Huiyang Zhou School of Electrical Engineering and Computer Science
2 Motivation It is a great challenge to build reliable computer systems with buggy software and unreliable hardware Software bugs account for 40% of system failures With technology scaling, transient faults are predicted to grow at least in proportion to the number of devices
3 Observation: Proposed Approach Soft-errors and software bugs manifest in similar ways. program localities can be used to detect abnormal behavior (soft-errors or bugs) during program execution. Unified Architectural Support: Utilize a type of value locality Limited Variance in Data Values (LVDV) to detect abnormal behavior A cache-like structure tracks LVDV locality and detects violations
4 Presentation Outline Using localities to detect abnormal behavior Limited variance in data values (LDVD) locality Proposed architectural design Locality based soft-error protection Locality based software-bug detection Summary
5 Using Locality to Detect Abnormal Behavior Execution results of instruction A: 20, 20, 20, 20,..., Execution results of instruction B: 1, 2, 3 1, 2, , 2, 3, 4 Violation of the constant or stride locality hints the possibility of an error, or a software bug.
6 Limited Variance in Data Values (LVDV) Variance between two values is defined as a simple XOR The variance specifies the fraction of bits which vary between the two values the rest of the bits do not change and can be protected
7 An Example of LVDV Locality Execution results of instruction A: 1, The result of 1 XOR 60
8 An Example of LVDV Locality Execution results of instruction A: 1, 60, The result of 60 XOR 12
9 An Example of LVDV Locality Execution results of instruction A: 1, 60, 12, The result of 12 XOR 40
10 An Example of LVDV Locality Execution results of instruction A: 1, 60, 12, 40, The result of 40 XOR 2
11 An Example of LVDV Locality Execution results of instruction A: 1, 60, 12, 40, 2,... No apparent pattern! However, the values are usually within a certain small range portion of result bits protected by LVDV
12 An Example of LVDV Locality Execution results of instruction A: 1, 60, 12, 40, 2,..., The result of 2 XOR
13 LVDV Captures Region Locality Memory addresses produced by instruction A: 0x , 0x x , 0x , 0x Heap memory addresses produced by instruction A exhibit no stride locality portion of result bits protected by LVDV
14 Proposed Architectural Support LVDV table PC Tag Confidence Encoded Variance Last Value The main structure in our architecture is an LVDV table.
15 Interaction of the LVDV table with the processor pipeline
16 Locality-Based Soft-Error Detection When an anomaly is detected, squash the pipeline as in a branch misprediction The anomaly is detected promptly and re-execution fixes the soft-error A single LVDV table can catch soft-errors in the computational logic, control logic such as decoder, renaming table, issue queue and operand selection logic
17 Benefits of Locality-Based Soft-Error Detection Provides information redundancy (no redundant execution required) Provides efficient, opportunistic protection of multiple hardware structures
18 Experimental Methodology Error injection into the Issue Queue and Functional Units Compare our approach to: Implicit Redundancy Through Reuse (IRTR) [Gomaa et al., ISCA 2005] Squash on L2-cache miss (SL2) [Weaver et al., ISCA 2004] Squash on Branch misprediction (BR-squash) [Wang et al., DSN 2005]
19 Strength of the LVDV Locality 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Percentage of Protected Result Bits 1K entries 4k entries 2K entries 8K entries 50 % gap bzip2 gzip perl twolf vpr gcc mcf parser vortex ammp art equake mesa swim wupwise Average
20 Performance Overhead 35.00% 30.00% 25.00% 20.00% 17 % 0.3% 0.0% 0.0% 2.3% 0.5% 0.0% Performance Overheads 0.7% 31 % 0.0% 0.6% 0.0% 0.0% 0.0% 2.1% 2.1% 0.7% LVDV SL2 BR-squash 15.00% 0.0% 0.0% 10.00% 5.00% 0.00% gap bzip2 gzip perl twolf vpr gcc mcf parser vortex ammp art equake mesa swim wupwise H_Mean
21 Soft-Error Protection of Issue Queues 50% 45% 40% 35% Protection of Issue Queue Percent Errors Removed by LVDV Percent Errors Removed by SL2 Percent Errors Removed by BR-squash 28 % 30% 25% 20% 15% 10% 5% 0% gap bzip2 gzip perl twolf vpr gcc mcf parser vortex ammp art equake mesa swim wupwise G_Mean LVDV removes 28% of critical errors MTTF improvement of 39%
22 Soft-Error Protection of Functional Units 70% 60% 50% Protection of Functional Units Percent errors removed by LVDV Percent error removed by IRTR 42 % 40% 30% 20% 10% 0% gap bzip2 gzip perl twolf vpr gcc mcf parser vortex ammp art equake mesa swim wupwise G_Mean LVDV removes up to 61% of critical errors and 42% on average MTTF improvement of up to 156% and 72% on average
23 Locality-Based Software Bug Detection Training phase LVDV table learns invariance information from multiple successful program runs OR from a single long run. Bug detection phase LVDV table detects anomalies during the failing run Anomalies are reported as possible root-causes of a software bug Misses in the LVDV table are also signaled as new-code anomalies We used a 4K entries table and tracked the variance in every store instruction
24 Locality-Based Software Bug Detection Shown to pinpoint latent program bugs Can detect bugs, which do not violate any programming rules Our approach can be viewed as a hardware implementation of DIDUCE [Hangal et al, ICSE 2002] Hardware offers the following advantages: Performance efficiency Binary compatibility Runtime monitoring
25 Case Study Incorrect Bounds Checking char newname[max]; /* convert the filename */ for(i=0;i<strlen(original);i++){ /*bug*/ if( isupper( original[i] ) ){ newname[i]= tolower(original[i]); continue; } newname[i] = original[i]; /*detection*/ } newname[i] = '\0'; /*detection*/ A buffer overflow in polymorph-0.4.0
26 Case Study Warm-up Phase char newname[max]; /* convert the filename */ for(i=0;i<strlen(original);i++){ /*bug*/ if( isupper( original[i] ) ){ newname[i]= tolower(original[i]); continue; } newname[i] = original[i]; /*detection*/ } newname[i] = '\0'; /*detection*/ &newname[3]: 7fffaf33 &newname[6]: 7fffaf36 &newname[11]: 7fffaf3b &newname[2]: 7fffaf32 &newname[17]: 7fffaf41 &newname[9]: 7fffaf39 &newname[5]: 7fffaf35 &newname[16]: 7fffaf40 conf: 15 prev_result: 7fffaf40 encoded variance: 7
27 Case Study Detection Phase char newname[max]; &newname[85]: 7fffaf85 /* convert the filename */ for(i=0;i<strlen(original);i++){ /*bug*/ if( isupper( original[i] ) ){ newname[i]= tolower(original[i]); continue; } newname[i] = original[i]; /*detection*/ } newname[i] = '\0'; /*detection*/ conf: 15 encoded variance: 7 variance: 8
28 Summary of Results 4K LVDV provides similar bug detection capabilities as software approach DIDUCE Also similar number of false-positives as DIDUCE Polymorph bc Ncompress (input 1) ncompress (input 2) gzip DIDUCE K LVDV
29 Summary Soft-errors and software bugs manifest in similar ways during execution We can use localities to detect both Proposed mechanism protects multiple structures from soft-errors ( 39% and 72% MTTF of IQ and FU) Similar bug detection capabilities to DIDUCE (a software based bug detection scheme)
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