Posts Tagged ‘compiler’

The compiler/interpreter distinction

September 8th, 2015 No comments

What is the difference between compiled and interpreted programs?

In the good-old-days the distinction was easy to make: compiled code is executed by hardware while interpreted code is executed by software.

These days it can be very difficult to decide whether a program will be executed by hardware or software, and in some cases both may occur. More complicated cpus implement some of their instructions in micro-code (software control of very low level hardware resources) and the virtual machines specified for software execution can be implemented in hardware (an interesting project for a group of talented students in their summer holidays wanting to learn about ASICs).

Some people make a distinction based on the abstraction level of the cpu specification, e.g., very high level abstraction means the code must be interpreted. In practice the implementation of a cpu specification in hardware or software is an economic decision (software may be slow, but its a lot cheaper to implement).

I think there is a compiler/interpreter distinction, but the difference is not about how code is executed (the hardware/software distinction is a convenient difference that is easy to explain).

The compiler/interpreter distinction is a difference of responsibility. Compilers treat programs like the Spartans treated their children, they are bundled into a file of the appropriate format and left for the Operating system to load into memory and point the cpu at the first instruction (a cpu’s one interest is executing the sequences of instructions pointed to by the program counter). Interpreters are more like dotting nannies, organizing the provision of memory and on call to provide access to the desired resources.

Sometimes a language is classified as an interpreted language. There is no such thing as an interpreted language, only languages which are much more easily implemented using an interpreter than a compiler.

The performance of the Spartan approach may be very desirable, but the cost of achieving it can be very high.

Actively maintained production compilers for middle-age languages

September 1st, 2015 10 comments

The owners of the Borland C++ compiler have stopped maintaining it. So we are now down to, by my counting, three four different production quality C++ compilers still being actively maintained (Visual C++ {the command line c1.exe, not the interactive IDE compiler}, GCC, LLVM and EDG); lots of companies repackage EDG and don’t talk about it.

How many production compilers for other middle-age languages are still being actively maintained?

Ada I think is now down to one (GNAT; I’m not sure of the status of what was the Intermetrics compiler).

Cobol has two+ (I’m not sure ow many internal compilers IBM has, some of which are really Microfocus) that I know of (Microfocus and Fujitsu {was ACUCobol}).

Fortran probably needs more than one hand to count its compilers. Nothing like having large engineering applications using the languages features supported by your compiler to keep the maintenance fees rolling in.

C still has lots of compilers (a C validation suite vendor told me many years ago that they had over 150 customers). Embedded processors can be a very tough target for the general purpose algorithms used in GCC and LLVM, so vendors with hand crafted compilers can still eek out a living.

Perl has one (which I find surprising).

R has one, but like Cobol it is not a fashionable language in compiler writing circles. Over the last couple of years there have been a few ‘play’ implementations and rumors of people creating a new production quality implementation.

Lisp has one or millions, depending on how you view dialects or there could be a million people with a different view on the identity of the 1.

Snobol-4 still has one (yes, I am a fan of this language).

There are lots of languages which have not yet reached middle-age, so its too soon to start counting how many actively supported compilers they still have in production use.

Open source: monoculture is more desirable than portability

March 8th, 2014 No comments

An oft repeated fable is that open source software is portable, all thanks to C and Unix. The reality is that open source lives in an environment that is evolving to become a monoculture that does not require portability, this is being driven by the law of the jungle.

First some background and history. Portability requires that source code have the same behavior on different platforms, or rather than programs built from that code have the same behavior, this requires that:

  • all compilers assign the same semantics to a given piece of code,
  • all operating systems include support the same set of libraries in the same way.

If you want portability across lots of compilers then Fortran has stood head and shoulders above the competition for decades. Now some C folk may point out that they have been compiling some large code base for decades, with few changes necessitated by compiler differences; yes this has been possible in certain niche markets where there is a dominant supplier who has a vested interest in not breaking customer code. C has a long history of widespread large variation in behavior of across compilers.

What about Cobol you ask? Cobol is all about data manipulation and unless you have data in the format expected by a Cobol program you have no need for that program. Nobody cares about portable Cobol programs unless they are also interested in portable data.

If you want portability across lots of operating systems the solution has always been to minimise the dependency on system/third-party library calls (to the extent of including source code for functionality often supported by an OS). The reason for minimising OS dependency is the huge variation in support for different libraries and a wide range of behaviors for supposedly the same functionality. But you say, Unix is an OS that did/does provide a common set of libraries that have the same behavior; no, this is history seen through rose tinted glasses as anyone who knows about the Unix wars will tell you.

In the last century, to experience ‘portability’ Unix developers had to live in a monoculture of either PDP-11s or Sun workstations.

Open source, as it existed in the 1970s, 80s and into the 90’s was Fortran code that ran on a surprisingly wide range of OS/cpu/compilers, along with a smattering of other languages. Back then there were not many software applications and when they did exist many were written in Fortran (Oracle being an early, lots of Fortran, example), this created a strong incentive for vendors to support a Fortran compiler that did things the same way as everybody else (which did not prevent them adding proprietary goodies to try and lure customers towards lock-in).

How did we get to today’s dominance of C and Unix? Easy, evolution at a rate that caused competitors to die out until there was a last man standing. That last man standing was gcc and Linux. The portability problem has been solved by removing the need to port code; it is compiled by the same compiler to run on the same cpu (Intel x86 family) to run under the same OS (Linux).

Of course some of today’s open source C is compiled using non-gcc compilers, but the percentage is small and specialised (a lot of the older code is portable because it used to exist in a multi-compiler/cpu/OS world and had to evolve into being portable). The gcc competitor, llvm, is working long and hard to ensure compatibility and somehow has to differentiate itself while being compatible, a tough fight for developer hearts and minds.

Differences in CPU characteristics are a big headache to any compiler writer wanting to support identical behavior across platforms; having a single cpu family as the market leader more or less solves this problem. ARM has become a major player in the CPU world, but it shares many developer visible characteristics with Intel x86 (e.g., 32-bit int, 64-bit long, pointer and ints are the same size and IEEE floating-point) and options are available for handling some of the other potential differences (e.g., right shift of signed integers).

The Unix wars have not gone away, they have moved to more far flung battlefields leaving behind some hard fought over common ground. Anybody who wants to see the scares left by these war only needs to look at the #ifs in system headers or the parameters selected inside .configure files.

Having everybody use the same compiler/cpu/OS saves having to make a huge time/money investment in making software portable, at least until the invention of photonic computers or the arrival of aliens (whose computers are unlikely to contain a CPU that shares Intel/ARM characteristics or have the same libraries as Linux).

C/Linux has not won in the sense that competitors have given up; in 20 years time the majority of open source in active use might be Javascript running inside a browser.

Survey of instruction selection

June 21st, 2013 1 comment

A well written survey of compiler instruction selection has just become available, the first major survey of this topic in 30 years! The academic outlook of the author is given away by the evaluation “…the technique appears to have had very limited impact as the citation count for the paper is low.” and coverage for the last 10 years does tend to thin out (but that could fill another 100 pages). Whatever your interest in compilers this survey is well worth a read.

Anybody reading a compiler book could be forgiven for thinking that instruction set selection was a minor issue; Gabriel Hjort Blindell counted 160 pages devoted to the topic out of 4,600 pages in seven well known compiler books. In a production compiler it is the parsing and semantics that consume 3% of the code with optimization and code generation making up the other 97%.

A 100 page survey of register allocation is also overdue (20 pages is a bit short).

Instruction set selection is one quarter of code generation, another quarter being register allocation and the remaining half being how these two are woven together (Hjort Blindell lists instruction scheduling as a third component and we could all argue for hours about whether this is another optimization, something that is spread over instruction selection/register allocation or a distinct component).

For a given choice of registers there are algorithms that will select the optimal code and for a given sequence of code there are algorithms that will select the optimal registers to use. Papers covering the optimal selection of both registers and instructions are thin on the ground; this is something of a black art that is picked up by building a production compiler.

Verified compilers and soap powder advertising

March 10th, 2013 6 comments

There’s a new paper out claiming to be about a formally-verified C compiler, it even states a Theorem about its abilities! If this paper appeared as part of a Soap powder advert the Advertising Standards Authority would probably require clarification of the claims. What clarifications might appear in the small print tucked away at the bottom of the ad?

  1. C source code is not verified directly, it is first translated to the formal notations used by the verification system; the software that performs this translation is assumed to be correct.
  2. The CompCert system may successfully translate programs containing undefined behavior. Any proof statements made about such programs may not be valid.
  3. The support tools are assumed to be correct; primarily the Coq proof assistant, which is written in OCaml.
  4. The CompCert system makes decisions about implementation dependent behaviors and any proofs only apply in the context of these decisions.
  5. The CompCert system makes decisions about unspecified behaviors and any proofs only apply in the context of these decisions.

Some notes on the small print:

The C source translator used by CompCert rarely gets mentioned in any of the published papers; what was done to check its accuracy (I have previously discussed some options)? Presumably the developers who wrote it tried very hard to make sure they did a good job, just like the authors of f2c, a Fortran to C translator, did. Connecting f2c as a front-end of the CompCert system gives us a verified Fortran compiler! I think the f2c translator is much more likely to be correct than the CompCert C source translator, it has been used by a lot more people, processed a lot more source and maintained over a longer period.

When they encounter undefined behavior in source code production C compilers sometimes generate code that has very unexpected behavior. Using the CompCert system will not avoid unexpected behavior in these situations; CompCert simply washes its hands for this kind of code and says all bets are off.

Proving the support tools correct would simply move the assumption of correctness to a different set of tools. I am not aware of any major effort to test whether the Coq system behaves as intended, but have not read all the papers describing it (the list of reported faults is does not appear to be publicly available); bugs have been found in the OCaml implementation.

Like all compilers that generate code, CompCert has to make implementation dependent decisions and select one of the possible unspecified behaviors. The C-Semantics tool generates all unspecified behaviors, rather than just one.

Only compiler vendor customers, not its users, count

January 23rd, 2013 3 comments

The hardest thing about working on compilers is getting somebody to pay you to do it (its a close run race against having the cpu instructions chop and change under you during initial development, but that’s another story). The major shift of compiler vendor business model from proprietary to open source has significant implications for users of compilers. Note I said user not customer, only one of them pays money. Under the commercial model there was usually a very direct connection between compiler user and customer (even in large organizations users rather than the manager who makes the purchase decision are often regarded by vendors as the customer), while under the Open source model most users are not customers (paying money for a distribution does not make you a customer of the people maintaining the compiler who probably don’t receive any of the money you spent).

Like all good businesses compiler vendors don’t want to make their customers unhappy. There is one way guaranteed to make all customers so unhappy that they will remember the experience for years; ship a new compiler release that breaks their existing code (this usually happens because their is a previously undetected bug in the code or because use is being made of an implemented defined/undefined part of the language {the compiler gets to decide what to do when it encounters such code}). Not breaking existing customer code is priority ONE in any commercial compiler development group.

Proprietary vendors have so many customers its almost impossible for them to know in advance what changes will break existing code and the only option is to be ultra conservative about adding new code optimizations (new optimizations can so easily change how source containing undefined behavior is processed). Ultra conservative is the polite term, management paranoia would be more accurate. There is another advantage to vendors for not breaking their customers’ code, they are protected against competition by new market entrants; a new vendor with a shiny go faster compiler doing all the optimizations the existing vendor was not willing to do in case it broke existing code will quickly find out that the performance improvements they offer are rarely big enough to tempt potential customers to switch compilers. Really, the only time companies switch compiler is when they have to port to a new platform to make a sale or their existing vendor goes bust.

Open source vendors (e.g., those commercially involved in support/maintenance of gcc or llvm) have relatively few customers (e.g., big companies paying them lots of money for specific reasons) and as always these customers want existing code to continue to work. If the customer is paying for a code generator for a previously unsupported processor then there probably isn’t any existing code for that processor; it is a fact of life that porting source to a new processor always involves work. Some Linux distributors (e.g., Suse and Redhat) are customers in the sense that they pay the salary of developers who spend a lot of their time in compiler maintenance/upgrades and presumably work to try and ensure that the code in their respective Linux distributions does not get broken.

Compiler users who are not customers don’t count on the code breakage front (well, count for very very little, if an update broke lots of different developers’ code and enough fuss was made there might well be an update than unbroke the previous one).

What can a user do if code that used to work ok is broken when compiled with a later version of the compiler? The obvious answer is to continue using the older version that produces the desired behavior, fixing the code causing the problem is a better answer (but might involve a lot of work). There is no point in flaming the compiler developers, you are not contributing towards their upkeep; Open source does not give users the consideration that a customer enjoys.

The oldest compiler still in production use is?

August 31st, 2012 No comments

What is the oldest compiler still in production use?

A CHILL compiler I worked on a long time ago has probably been in production use for 30 years now.

Code gets added and deleted from production software all the time, how might ‘oldest’ be measured? I propose using the mean age of every line of code, including comments, where the age of a line is reset to zero when it is modified in any way (excluding code formatting).

The following are two environmental factors that enable a production compiler to get very old:

  • a relatively obscure language: popular languages have new compilers written for them (compiler death through competition) or have new features added to them (requiring new lines of code which could even displace ‘aged’ code),
  • a very long lived application associated with the language: obscure languages tend to be very quickly abandoned in the dust of history unless they have a symbiotic relationship with an important application,
  • very long lived host hardware and target processor: changing either often requires substantial new code or a move to a newer compiler. For ancient the only candidate is the IBM 370 and just really old the Intel 80×80, Zilog Z80.

Virtual machines provide a mechanism to be host hardware independent. The Micro Focus Cobol had a rewrite in the early 1990s (it might have had others since) and I don’t think UCSD Pascal I.5 is still used for production work.

Fortran is an evolving language and very popular in some application domains. I doubt there are any (mean age) old Fortran compilers in production use.

Why do I put forward the ITT (in its International Telephone & Telegraph days, these bits subsequently sold off) CHILL compiler as potentially the oldest compiler currently in production use?

  • Obscure language and long lived application (telephone switching software),
  • host hardware was IBM 370 family, target processor Intel 8086 (later updated to support 80386),
  • large development team and very small support team (i.e., lots of old code and small changes over the years),
  • single customer, i.e., no push to add features to attract new customers or keep existing ones.

My last conversation with anybody associated with this compiler was a chance meeting over 10 years ago, so I might be a bit out of date.

30+ year old source code for compilers can be downloaded (e.g., the original PDP 11 C compiler) but these compilers are not in production use (forgotten about military installations anybody?)

I welcome other proposals for the oldest compiler currently in production use.

Go faster R for Google’s summer of code 2012

March 28th, 2012 5 comments

The R Foundation has been accepted for Google’s summer of code and I thought I would suggest a few ideas for projects. My interests are in optimization and source code analysis, so obviously the suggestions involve these topics.

There are an infinite number of possible optimizations that can be applied to code (well, at least more than the number of atoms in the known universe). The first job for any optimization project is to find the common characteristics of the code; once these are known the available resources can be concentrated on improving the performance of these common cases (as they evolve optimizers necessarily attack less frequently occurring constructs and in rare cases address a previously unnoticed common pattern of behavior).

What are the common characteristics of R programs? I have no idea and have not seen any published empirical analysis on the subject. Analysing the characteristics of the R source code ecosystem would make a very good summer project. The analysis could be static, based purely on the source, or dynamic, looking at the runtime characteristics. The purpose of analyse is to gain a general understanding of the characteristics of R code and to investigate whether specific kinds of optimizations might be worthwhile. Often optimizations are suggested by the results of the analysis and in some cases optimization possibilities that were thought to be worthwhile turn out to have little benefit. I will stick my neck out and suggest a few optimizations that I think might be worthwhile.

  • Reducing object copying through last usage analysis. In R function arguments are passed using call-by-value, that is a copy of the argument is made and passed to the called function. For large arguments call-by-value is very time consuming and if the value of the argument is not used after the called function returns the copy operation is redundant. I think it would be a worthwhile optimization for the R compiler to replace call-by-value with call-by-reference in those cases where the current argument is not read again and is modified during the call (the R implementation uses copy-on-write so there is overhead minimal overhead if the argument is only ever read); analysis is needed to verify this hunch.
  • Operations on short vectors. Many processors have instructions that simultaneously perform the same operation on a small number of values (e.g., the Intel/AMD SSE instructions). If it is possible to figure out that the two vectors involved in an add/subtract/multiple/etc are short, the same length, do not contain any NA, then a ‘short-operation’ instruction could be generated (when running on processors without the necessary support the R interpreter would implement these the same way as the longer forms). Analysis is needed to find out how often short vector operations occur in practice.
  • Do R programs spend most of their time executing in C/Fortran routines or in R code? If the answer is C/Fortran and there is some set of functions that are called frequently then it may be worthwhile having versions of these that are tuned to the common case (whatever that might be). If the answer is R then what is the distribution pattern of R operations? There is a lot that can be done to speed up the R interpreter, but that project will need a lot more effort than is available in a summer of code and we need to get some idea of what the benefits for the general population might be.

To increase coverage of R usage the measurement tools should be made available for people to download and run on their own R code, and hopefully forwarding the output back some central collection point. For maximum portability this means writing the static analysis tools in R. By their very nature the dynamic analysis measurements have to be made via changes to the R system itself, getting users to download and use prebuilt binaries (or building from source) has always been fraught with problems; it is always hard o get users to buy into helping out with dynamic measurements.

Sophisticated static analysis consumes lots of compute resources. However, R programs tend to be short, so the required resources are unlikely to be that great in R’s case; even writing the analysis in R should not cause the resource requirements to be that excessive.

The only other language likely to share many of R’s language usage characteristics that I can think is APL. There have been a few published papers on APL usage, but these were not that wide ranging and probably not of much use. Perhaps somebody who worked for a now defunct APL compiler company has a copy of in-house performance analysis reports they can make available.

Type compatibility the hard way

January 14th, 2012 No comments

When writing in assembly language it is possible to operate on a sequence of bits as if it were an unsigned integer one moment and a floating-point number the next; it is the developer’s responsibility to ensure that a given sequence of bits is operated on in a consistent manner. The concept of type was initially introduced into computer languages to provide information to compilers, enabling them to generate the appropriate instructions for values having the specified type and where necessary to convert values from the representation used by one type to the representation used by a different type. At this early stage language designers tended to keep things simple and to think in terms of what made sense at the machine representation level when deciding which type conversions to permit (PL/1 was a notable exception and the convolutions that occurred to perform some type conversions are legendary).

It took around 10 years for high level languages to evolve to the point where developers had the ability to create their own named types; Pascal being an early, very well known and stand out example. Once developers could create their own types it became necessary to come up with general rules specifying when a compiler must treat two different types as compatible (i.e., be required to generate code to support some set of operations between variables having these two different types).

Most language designers chose the simple option; a type is compatible with another type if it has the same name (scoping/namespace/lookup rules effectively meant that “same name” was effectively the same as “same definition”). This simple option generally included various exceptions for the arithmetic types; developers did not like having to insert explicit casts for what they considered to be obvious conversions (languages such as Ada/CHILL provided a mechanism for developers to specify that a newly defined arithmetic type really was a completely new type that was not compatible with any other arithmetic type, an explicit cast could change this).

One of the few languages which took a non-simple approach to type compatibility was CHILL, a language for which I once spent over a year writing the semantics phase of a compiler. CHILL uses what is known as structural compatibility, i.e., essentially two types are compatible if they have the same layout in memory (the language definition actually uses the terms similar and equivalent rather than compatible and uses mode rather than type, here I will follow modern general terminology). This has obvious advantages when there is a need to overlay types used in different parts of a program onto the same location in storage (note, no requirements on the fields being the same). CHILL definitions look like a mixture of C and Pascal, unless you know PL/1 they can look odd to the uninitiated (I think I’ve got them right, my CHILL is very rusty), T_1 and T_2 are compatible:

T_1 = struct (               T_2 = struct (
      f1 :int;                     f3 :int;
      f2 :int;                     f4 :int;
      );                           );

Structural compatibility enables the creation some rather unusual compatible types, such as the following three types all being pair-wise compatible (the keyword ref is use to specify pointer types):

T_3 = struct (               T_4 = struct (             T_5 = struct (
      f1 :int;                     f4 :int;                   f7 :int;
      f2 :ref T_3;                 f5 :ref T_4;               f8 :ref T_5;
      f3 :ref T_4;                 f6 :ref T_3;               f9 :ref T_5;
      );                           );                         );

Because types can be recursive it is possible for the compatibility checking code in the compiler to end up having to type check the type it is currently checking. The solution adopted by many CHILL compilers (not that there were ever many) was to associate an is_currently_being_checked flag with every type’s symbol table entry, if during compatibility checking this flag has value TRUE for both types then they are both compatible otherwise the flag is set to TRUE for both types and checking continues (all flags are set to FALSE at the end of compatibility checking).

To check T_3 and T_4 In the above code set the is_currently_being_checked flag to TRUE and iterate over the fields in each record. The first field pair have the same type, the second field pair are pointers to types we are already checking and therefore compatible, as are the third field pair, so the types are compatible. Checking T_3 and T_5 requires a second iteration through T_5 because of the pointer to T_4 which does not yet have its is_currently_being_checked flag set.

Yours truely discovered that one flag was not sufficient to do fully correct compatibility checking. It is necessary to maintain a stack of locations (e.g., the structure field or procedure parameter where compatibility checking has to recurse to check a user defined type) in the two types being compared in order to detect that some types were not compatible. In the following example (involving pointer to procedure types; which is longer than I remember the actual instance I first discovered being, but I had to create it again from vague memories and my CHILL expertise has faded; suggestions welcome) types A and B would be considered compatible using the is_currently_being_checked flag approach because by the time the last parameter is checked both symbol table flags have been set. You can see by inspection that types X and Y are not compatible (they have a different number of parameters to start with). Looking at the stack of previous compatibility checks for A/B would show that no X/Y compatibility check had yet been made and one would be needed for the third parameter (which would fail):

A = proc(X, Y,            X);
B = proc(C, proc(A, int), Y);
C = proc(E);
D = proc(A);
E = proc(proc(X, proc(A, int), X));
X = proc(D);
Y = proc(A, int);

The potential for complexity created by the use of structural compatibility is one reason why its use is rare. While it is possible to rationalize that CHILL was targeted at embedded telecommunication systems containing lots of code where memory costs can be significant, I suspect that those involved had a hardware mentality and a poor grasp of practical software engineering issues.

Incidentally, the design of the llvm type checking system relies on using an equality test to check for type equality. While this decision will increase the difficulty of integrating languages that use structural type compatibility into llvm, these languages are probably sufficiently rare that it is much more cost effective to make it simple to implement the more common languages.

Where did type compatibility go next? Well, over the last 20 years the juggernaut of object oriented design has pretty much excluded sophisticated non-OO type systems from mainstream languages (e.g., C++ and Java), but that is a topic for another article.

Optimizing floating-point expressions for accuracy

December 15th, 2011 3 comments

Floating-point arithmetic is one topic that most compiler writers tend to avoid as much as possible. The majority of programs don’t use floating-point (i.e., low customer demand), much of the analysis depends on the range of values being operated on (i.e., information not usually available to the compiler) and a lot of developers don’t understand numerical methods (i.e., keep the compiler out of the blame firing line by generating code that looks like what appears in the source).

There is a scientific and engineering community whose software contains lots of floating-point arithmetic, the so called number-crunchers. While this community is relatively small, many of the problems it works on attract lots of funding and some of this money filters down to compiler development. However, the fancy optimizations that appear in these Fortran compilers (until the second edition of the C standard in 1999 Fortran did a much better job of handling the minutia of floating-point arithmetic) are mostly about figuring out how to distribute the execution of loops over multiple functional units (i.e., concurrent execution).

The elephant in the floating-point evaluation room is result accuracy. Compiler writers know they have to be careful not to throw away accuracy (e.g., optimizing out what appear to be redundant operations in the Kahan summation algorithm), but until recently nobody had any idea how to go about improving the accuracy of what had been written. In retrospect one accuracy improvement algorithm is obvious, try lots of possible combinations of the ways in which an expression can be written and pick the most accurate.

There are lots of ways in which the operands in an expression can be paired together to be operated on; some of the ways of pairing the operands in a+b+c+d include (a+b)+(c+d), a+(b+(c+d)) and (d+a)+(b+c) (unless the source explicitly includes parenthesis compilers for C, C++, Fortran and many other languages (not Java which is strictly left to right) are permitted to choose the pairing and order of evaluation). For n operands (assuming the operators have the same precedence and are commutative) the number is combinations is C_n * n! where C_n is the n’th Catalan number. For 5 operands there are 1680 combinations of which 120 are unique and for 10 operands 1.76432*10^10 of which 4.66074*10^7 are unique.

A recent study by Langlois, Martel and Thévenoux analysed the accuracy achieved by all unique permutations of ten operands on four different data sets. People within the same umbrella project are now working on integrating this kind of analysis into a compiler. This work is another example of the growing trend in compiler research of using the processing power provided by multiple cores to use algorithms that were previously unrealistic.

Over the last six years or so there has been lot of very interesting floating-point work going on in France, with gcc and llvm making use of the MPFR library (multiple-precision floating-point) for quite a while. Something very new and interesting is RangeLab which, given the lower/upper bounds of each input variable to a program (a simple C-like language) computes the range of the outputs as well as ranges for the roundoff errors (the tool assumes IEEE floating-point arithmetic). I now know that over the range [800, 1000] the expression x*(x+1) is a lot more accurate than x*x+x.

Update: See comment from @Eric and my response below.