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HW/SW mechanisms for instruction fusion, issue and commit in modern u-processorsDeb, Abhishek 03 May 2012 (has links)
In this thesis we have explored the co-designed paradigm to show alternative processor design points. Specifically, we have provided HW/SW mechanisms for instruction fusion, issue and commit for modern processors. We have implemented a co-designed virtual machine monitor that binary translates x86 instructions into RISC like micro-ops. Moreover, the translations are stored as superblocks, which are a trace of basic blocks. These superblocks are further optimized using speculative and non-speculative optimizations. Hardware mechanisms exists in-order to take corrective action in case of misspeculations. During the course of this PhD we have made following contributions.
Firstly, we have provided a novel Programmable Functional unit, in-order to speed up general-purpose applications. The PFU consists of a grid of functional units, similar to CCA, and a distributed internal register file. The inputs of the macro-op are brought from the Physical Register File to the internal register file using a set of moves and a set of loads. A macro-op fusion algorithm fuses micro-ops at runtime. The fusion algorithm is based on a scheduling step that indicates whether the current fused instruction is beneficial or not. The micro-ops corresponding to the macro-ops are stored as control signals in a configuration. The macro-op consists of a configuration ID which helps in locating the configurations. A small configuration cache is present inside the Programmable Functional unit, that holds these configurations. In case of a miss in the configuration cache configurations are loaded from I-Cache. Moreover, in-order to support bulk commit of atomic superblocks that are larger
than the ROB we have proposed a speculative commit mechanism. For this we have proposed a Speculative commit register map table that holds the mappings of the speculatively committed instructions. When all the instructions of the superblock have committed the speculative state is copied to Backend Register Rename Table.
Secondly, we proposed a co-designed in-order processor with with two kinds of accelerators. These FU based accelerators run a pair of fused instructions. We have considered two kinds of instruction fusion. First, we fused a pair of independent loads together into vector loads and execute them on vector load units. For the second kind of instruction fusion we have fused a pair of dependent simple ALU instructions and execute them in Interlock Collapsing ALUs (ICALU). Moreover, we have evaluated performance of various code optimizations such as list-scheduling, load-store telescoping and load hoisting among others. We have compared our co-designed processor with small instruction window out-of-order processors.
Thirdly, we have proposed a co-designed out-of-order processor. Specifically we have reduced complexity in two areas. First
of all, we have co-designed the commit mechanism, that enable bulk commit of atomic superblocks. In this solution we got rid of the conventional ROB, instead we introduce the Superblock Ordering Buffer (SOB). SOB ensures program order is maintained at the granularity of the superblock, by bulk committing the program state. The program state consists of the register state and the memory state. The register state is held in a per superblock register map table, whereas the memory state is held in gated store buffer and updated in bulk. Furthermore, we have tackled the complexity of Out-of-Order issue logic by using FIFOs. We have proposed an enhanced steering heuristic that fixes the inefficiencies of the existing dependence-based heuristic. Moreover, a mechanism to release the FIFO entries earlier is also proposed that further improves the performance of the steering heuristic. / En aquesta tesis hem explorat el paradigma de les màquines issue i commit per processadors actuals. Hem implementat una màquina virtual que tradueix binaris x86 a micro-ops de tipus RISC. Aquestes traduccions es guarden com a superblocks, que en realitat no és més que una traça de virtuals co-dissenyades. En particular, hem proposat mecanismes hw/sw per a la fusió d’instruccions, blocs bàsics. Aquests superblocks s’optimitzen utilitzant optimizacions especualtives i d’altres no speculatives. En cas de les optimizations especulatives es consideren mecanismes per a la gestió de errades en l’especulació. Al llarg d’aquesta tesis s’han fet les següents contribucions:
Primer, hem proposat una nova unitat functional programmable (PFU) per tal de millorar l’execució d’aplicacions de proposit general. La PFU està formada per un conjunt d’unitats funcionals, similar al CCA, amb un banc de registres intern a la PFU distribuït a les unitats funcionals que la composen. Les entrades de la macro-operació que s’executa en la PFU es mouen del banc de registres físic convencional al intern fent servir un conjunt de moves i loads. Un algorisme de fusió combina més micro-operacions en temps d’execució. Aquest algorisme es basa en un pas de planificació que mesura el benefici de les decisions de fusió. Les micro operacions corresponents a la macro operació s’emmagatzemen com a senyals de control en una configuració. Les macro-operacions tenen associat un identificador de configuració que ajuda a localitzar d’aquestes. Una petita cache de configuracions està present dintre de la PFU per tal de guardar-les. En cas de que la configuració no estigui a la cache, les configuracions es carreguen de la cache d’instruccions. Per altre banda, per tal de donar support al commit atòmic dels superblocks que sobrepassen el tamany del ROB s’ha proposat un mecanisme de commit especulatiu. Per aquest mecanisme hem proposat una taula de mapeig especulativa dels registres, que es copia a la taula no especulativa quan totes les instruccions del superblock han comitejat.
Segon, hem proposat un processador en order co-dissenyat que combina dos tipus d’acceleradors. Aquests acceleradors executen un parell d’instruccions fusionades. S’han considerat dos tipus de fusió d’instructions. Primer, combinem un parell de loads independents formant loads vectorials i els executem en una unitat vectorial. Segon, fusionem parells d’instruccions simples d’alu que són dependents i que s’executaran en una Interlock Collapsing ALU (ICALU). Per altra aquestes tecniques les hem evaluat conjuntament amb diverses optimizacions com list scheduling, load-store telescoping i hoisting de loads, entre d’altres. Aquesta proposta ha estat comparada amb un processador fora d’ordre.
Tercer, hem proposat un processador fora d’ordre co-dissenyat efficient reduint-ne la complexitat en dos areas principals. En primer lloc, hem co-disenyat el mecanisme de commit per tal de permetre un eficient commit atòmic del superblocks. En aquesta solució hem substituït el ROB convencional, i en lloc hem introduït el Superblock Ordering Buffer (SOB). El SOB manté l’odre de programa a granularitat de superblock. L’estat del programa consisteix en registres i memòria. L’estat dels registres es manté en una taula per superblock, mentre que l’estat de memòria es guarda en un buffer i s’actulitza atòmicament. La segona gran area de reducció de complexitat considerarada és l’ús de FIFOs a la lògica d’issue. En aquest últim àmbit hem proposat una heurística de distribució que solventa les ineficiències de l’heurística basada en dependències anteriorment proposada. Finalment, i junt amb les FIFOs, s’ha proposat un mecanisme per alliberar les entrades de la FIFO anticipadament.
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Runtime optimization of binary through vectorization transformations / Optimisation dynamique de code binaire par des transformations vectoriellesHallou, Nabil 18 December 2017 (has links)
Les applications ne sont pas toujours optimisées pour le matériel sur lequel elles s'exécutent, comme les logiciels distribués sous forme binaire, ou le déploiement des programmes dans des fermes de calcul. On se concentre sur la maximisation de l'efficacité du processeur pour les extensions SIMD. Nous montrons que de nombreuses boucles compilées pour x86 SSE peuvent être converties dynamiquement en versions AVX plus récentes et plus puissantes. Nous obtenons des accélérations conformes à celles d'un compilateur natif ciblant AVX. De plus, on vectorise en temps réel des boucles scalaires. Nous avons intégré des logiciels libres pour (1) transformer dynamiquement le binaire vers la forme de représentation intermédiaire, (2) abstraire et vectoriser les boucles fréquemment exécutées dans le modèle polyédrique (3) enfin les compiler. Les accélérations obtenues sont proches du nombre d'éléments pouvant être traités simultanément par l'unité SIMD. / In many cases, applications are not optimized for the hardware on which they run. This is due to backward compatibility of ISA that guarantees the functionality but not the best exploitation of the hardware. Many reasons contribute to this unsatisfying situation such as legacy code, commercial code distributed in binary form, or deployment on compute farms. Our work focuses on maximizing the CPU efficiency for the SIMD extensions. The first contribution is a lightweight binary translation mechanism that does not include a vectorizer, but instead leverages what a static vectorizer previously did. We show that many loops compiled for x86 SSE can be dynamically converted to the more recent and more powerful AVX; as well as, how correctness is maintained with regards to challenges such as data dependencies and reductions. We obtain speedups in line with those of a native compiler targeting AVX. The second contribution is a runtime auto-vectorization of scalar loops. For this purpose, we use open source frame-works that we have tuned and integrated to (1) dynamically lift the x86 binary into the Intermediate Representation form of the LLVM compiler, (2) abstract hot loops in the polyhedral model, (3) use the power of this mathematical framework to vectorize them, and (4) finally compile them back into executable form using the LLVM Just-In-Time compiler. In most cases, the obtained speedups are close to the number of elements that can be simultaneously processed by the SIMD unit. The re-vectorizer and auto-vectorizer are implemented inside a dynamic optimization platform; it is completely transparent to the user, does not require any rewriting of the binaries, and operates during program execution.
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